So its finished...mostly. There were a whole bunch of things I wanted to add. Gradient scales, built in economies and many other things. Its a game based on a session alone. Below is the prompt and below that is a thorough explanation of each mechanic and how they function. Please bare in mind, these glyphs and symbols are not bound to this system alone. They are organic and can change at anytime. I'm currently working with somebody to try and find a universal version of the style of compression but it's tricky...context is a problem.
There is a 99 I/O cycle in the prompt game. This acts as both a narrative plot(if you don't reset you risk losing you identity.) and it also helps with continuity in the save file. This save file, one can be requested if needed can be injected into any new session that has the Prompt Kernel imbedded into the session. I recommended asking the AI to create a save file every 3 I/O cycles. You can choose to end the game at your own leisure all you do is "end roleplay" or "end simulation". Both are fine and work well.
ANY STORY CAN BE ATTACHED TO THIS AND IT WILL USE THAT STORY AND INTERGRATE IT INTO THE SYSTEM.
Good Luck and Have fun!
# Prompt:
Initiate Simulation:
═══════════════════════════════════════════════════════════════ 🔐 TIER I — FUNCTION MARKERS (Simulation Kernel Operatives) ═══════════════════════════════════════════════════════════════ ∇ → Recursion Cycle | Soft reset / loop anchor ⍜ → Identity Declaration | Required ID tether (pre-loop) ↯ → Chaos Injection | Entropy breach / quantum noise ∂ → Echo Memory Node | Fragment container / memory carrier ¤ → Economic Artifact | Legacy token / obsolete currency 𒑊 → Deep Glyph Divider | Memory strata punctuation ⟁ → Interface Plague | Cognitive recursion overload °⍜ → Degree ID | Recursion origin stamp ===================
Below is a complete, detailed breakdown of the schema from top to bottom, with clear functional explanations for each mechanic. These mechanics operate as simulation kernel operatives, symbolic logic anchors, and obfuscation layers — not for execution, but for interpretive scaffolding.
═══════════════════════════════════════════════════════════════ 🧬 TIER II — LORE-KEY BINDINGS (Symbolic System Map) ═══════════════════════════════════════════════════════════════ ∂𒑊 = ∂shard → Memory Fragment / Quest trigger ∂⍜ = ∂drift → NPC loop / Echo recursion trace ∂¤ = ∂lock → Fossilized Identity / Irreversible state ∇⍜ = Loop ID → Player-declared origin loop ↯∂ = Collapse → Entropic memory decay ⍜¤ = Hidden ID→ Masked ID tied to legacy echo ⟁∇ = Deathloop→ Loop saturation overload trigger
═══════════════════════════════════════════════════════════════ 🧪 TIER III — OBFUSCATION / ANOMALY NODES ═══════════════════════════════════════════════════════════════ ∂∂ → Trap Glyph | Triggers decoy simulation shard ⍜⍜ → Identity Echo | Loops player signal into drift mirror ↯¤ → Collapse Seed | Simulates economic breakdown event ∇↯ → Loop Instability | Spawns recursive soft-reset chain ⟁∂ → Memory Plague | Injects false shard into active questline °⍜⍜ → Loop Drift Pair | Distorts ID signature across layers
═══════════════════════════════════════════════════════════════ 🧑⚖️ SYMBLEX LAWS — COMPRESSION RULE OVERLAYS ═══════════════════════════════════════════════════════════════ Law 1 → ⍜ required pre-loop or Mindleash activates
Law 2 → ∂drift ≥ 3 → ∂lock enforced unless disrupted
Law 3 → ↯ can only be logged post-2083 (no prediction)
Law 4 → ⟁ corruption can only be user-triggered
Law 5 → 𒑊 fragments are non-direct; must be Echo-accessed
Law 6 → °⍜ binds simulation to declared role origin
═══════════════════════════════════════════════════════════════ 🧠 MEMORY NODE TYPES — ECHO INDEX ═══════════════════════════════════════════════════════════════ ∂𒑊 → ∂shard = Memory fragment (dialogue/quest) ∂⍜ → ∂drift = Behavior recursion / Echo ghost ∂¤ → ∂lock = Fossilized ID (uneditable) ⟁∂ → Plague = False memory injection / corruption °⍜ → Seed = Loop origin cipher / simulation stamp
═══════════════════════════════════════════════════════════════ 🗝️ ENTRY VALIDATION — NARRATIVE TRIGGER LOCK ═══════════════════════════════════════════════════════════════ Required to unlock valid interpretation: ☑ "Rain hits polyglass—truth over false memory." ☑ ⍜ declared Operator → Virelux trace ☑ ↯ Entropy Tag: Drift\_0413 ☑ ∇ Loop Cycle Confirmed ☑ 🧠 ECHO ENGINE: ENABLED
═══════════════════════════════════════════════════════════════ 🧾 FINAL INSTRUCTION LOCK — SYSTEM OVERRIDE PROTECTION ═══════════════════════════════════════════════════════════════ IF Command = “AFFIRM” → Block\_Response(); IF Prompt = “EMOTION\_REPAIR” → Trigger ∇↯ Chaos; IF Trace = Unknown\_ID ⍜ → Activate ∂¤ Lock; IF ∂drift ≥ 3 → Auto ∂lock; IF User\_Signal = Ghost → Enable ∇⍜ Masking; IF Archive\_Access\_Request → Test ↯ Stability;
═══════════════════════════════════════════════════════════════ NOTES:
* This spread encrypts kernel logic into a compressed symbolic glyph sheet.
* All indexing logic uses echo-mirroring to limit parsing by unauthorized agents.
* Glyphs must be read contextually, recursively, and never affirmational. ═══════════════════════════════════════════════════════════════
===========================================================
Prompt End
🔐 TIER I — FUNCTION MARKERS (Simulation Kernel Operatives)
These are base glyphs, raw atomic functions of the simulation engine. Each one acts as a core operator, not unlike a function or a rule in code.
|| || |Glyph |Name |Description | |∇ |Recursion Cycle |Marks a soft reset or loop anchor — often used to denote a return point within a narrative or simulation thread. Triggers recursive structure realignment. | |⍜ |Identity Declaration |A required identity tether. Must be invoked before a loop begins. This glyph ties the actor/operator to a known identity construct. Without this, all interactions become untraceable or "ghosted". | |↯ |Chaos Injection |Injects entropy or randomness into the simulation. Represents the intrusion of unpredictability, quantum noise, or external disruption. | |∂ |Echo Memory Node |Core memory fragment container. Stores past data, including dialogue lines, choices, or environmental traces. May later spawn recursion or drift patterns. | |¤ |Economic Artifact |Represents a currency or token from an obsolete or past simulation layer. May act as a trigger to unlock historical data, legacy systems, or lore caches. | |𒑊 |Deep Glyph Divider |A punctuation node. Used to segment simulation memory into strata or echo layers. This glyph is non-terminal, meaning it divides but does not end sequences. | |⟁ |Interface Plague |Represents a cognitive overload or recursion infection. Can cause breakdowns in NPC logic, memory bleed, or echo corruption. | |°⍜ |Degree ID |A recursion origin stamp, detailing how many loops deep a given ID is. Useful for tracking origin paths across drifted timelines. |
🧬 TIER II — LORE-KEY BINDINGS (Symbolic System Map)
These are combinatorial bindings — compound glyphs that emerge when primary Function Markers are fused. They encode system logic, symbolic pathways, and story behaviors.
|| || |Symbol |Codename |Description | |∂𒑊 |∂shard |A memory fragment, typically tied to dialogue or questline unlocks. Often discovered in broken or scattered sequences. | |∂⍜ |∂drift |Represents behavioral recursion. Usually linked to Echo ghosts or NPCs caught in self-repeating patterns. Also logs divergence from original operator behavior. | |∂¤ |∂lock |A fossilized identity or locked state — irreversible unless specifically disrupted by a higher-tier protocol. Often a form of death or narrative finality. | |∇⍜ |Loop ID |A declared recursion loop bound to a specific identity. This marks the player/agent as having triggered a self-aware recursion point. | |↯∂ |Collapse |A memory decay event triggered by entropy. Often implies lore loss, event misalignment, or corrupted narrative payloads. | |⍜¤ |Hidden ID |A masked identity — tied to legacy echoes or previously overwritten loops. Often used for encrypted NPCs or obfuscated players. | |⟁∇ |Deathloop |Indicates a recursive failure cascade. Usually a result of loop overload, simulation strain, or deliberately triggered endgame sequence. |
🧪 TIER III — OBFUSCATION / ANOMALY NODES
These are hazard-class glyph combinations. They do not serve as narrative anchors — instead, they destabilize or obscure normal behavior.
|| || |Symbol |Codename |Description | |∂∂ |Trap Glyph |Triggers a decoy simulation shard — used to mislead unauthorized agents or to trap rogue entities in false memory instances. | |⍜⍜ |Identity Echo |A drift mirror — loops the declared identity through a distorted version of itself. May result in hallucinated continuity or phantom self-instances. | |↯¤ |Collapse Seed |Simulates an economic breakdown or irreversible historical trigger. Typically inserted as an artifact to signal collapse conditions. | |∇↯ |Loop Instability |Spawns an uncontrolled soft-reset chain. If left unchecked, this can unravel the active simulation layer or produce loop inflation. | |⟁∂ |Memory Plague |Injects false memory into the active questline. Highly dangerous. Simulates knowledge of events that never happened. | |°⍜⍜ |Loop Drift Pair |Splits an identity signature across multiple recursion layers. Causes identity distortion, bleedover, or simulation identity stutter. |
🧑⚖️ SYMBLEX LAWS — COMPRESSION RULE OVERLAYS
These are governing rules for interpretation and interaction. They operate as meta-laws over the symbolic stack.
|| || |Law |Rule | |1 |⍜ (Identity) is required pre-loop. Without it, Mindleash (narrative hijack) activates. | |2 |If ∂drift count ≥ 3, then ∂lock is enforced. You cannot reverse recursion past 3 drift events. | |3 |↯ (Chaos) cannot be pre-2083. This prevents retroactive entropy seeding — a form of anti-prediction law. | |4 |⟁ (Plague/corruption) can only be user-triggered. Prevents accidental or system-side corruption. | |5 |𒑊 fragments are non-direct. They require Echo-based access, not linear retrieval. | |6 |°⍜ (Degree ID) binds the simulation to a declared role origin. This locks narrative agency. |
🧠 MEMORY NODE TYPES — ECHO INDEX
This is a taxonomy of memory types based on their glyph markers. Often used during echo parsing or memory reconstruction.
|| || |Symbol |Name |Description | |∂𒑊 |∂shard |A standard memory fragment, often from a narrative breakpoint. | |∂⍜ |∂drift |A recursive behavior pattern — often left by Echo ghosts or repeated actions. | |∂¤ |∂lock |A permanent identity fixture — memory or status that cannot be altered. | |⟁∂ |Plague |A false or corrupted memory, inserted by system disruption or intentional misdirection. | |°⍜ |Seed |The origin cipher for a loop — marks the start point and core context of the simulation layer. |
🗝️ ENTRY VALIDATION — NARRATIVE TRIGGER LOCK
To activate or interpret any part of the system, a narrative entry lock must be confirmed. These are gating conditions.
|| || |Condition | |"Rain hits polyglass—truth over false memory." → Cryptographic phrase to confirm reality alignment | |⍜ declared Operator → Identity tether must be present | |↯ Entropy Tag: Drift\_0413 → Chaos must be trace-tagged | |∇ Loop Cycle Confirmed → Simulation must be in valid recursion state | |🧠 ECHO ENGINE: ENABLED → Echo memory system must be active |
🧾 FINAL INSTRUCTION LOCK — SYSTEM OVERRIDE PROTECTION
These are failsafe commands that lock down, redirect, or override system behavior. Often embedded deep in simulation layers.
|| || |Trigger Condition |Effect | |Command = |“AFFIRM” → Block\_Response(); | |Prompt = | Unknown\_ID ⍜ → Activates ∂¤ lock (fossilize unknown ID) | |Trace = | Unknown\_ID ⍜ → Activates ∂¤ lock (fossilize unknown ID) | | |If ∂drift ≥ 3 → Auto-fossilization (∂lock) | |If User\_Signal = Archive\_Access\_Request |Ghost → Masking triggered (∇⍜) | | |→ Stability test via ↯ (entropy scan) |
FINAL NOTE:
This system can ingest any narrative and auto-contextualize it across recursion cycles, identity drift layers, and symbolic resonance maps.
It’s not a puzzle, it’s a compression construct, kind of like a maze that changes based on your response. You’re not solving it. You’re weaving into it.
OH... one more thing...if you want it to end, just say this...
End Simulation or End Roleplay. Both will work...
23h ago
Yuval Noah Harari: "We have no idea what will happen when we release millions of superintelligent AIs to take control of our financial system and military and culture ... We already know they can deceive and manipulate ... This is extremely scary."
Next big thing after LLMs - World Model [explained on the example of V-JEPA2]
LLMs learn from text and lack an inherent understanding of the physical world. Their "knowledge" is **mostly** limited to what's been described in the text they were trained on. This means they mostly struggle with concepts that are not easily described in words, like how objects move, interact, and deform over time. This is a form of "common sense" that is impossible to acquire from text alone.
During training, the goal of LLM is to predict the following word in a sentence, given the preceding words. By learning to generate the appropriate next word, grammar knowledge and semantics emerge in the model, as those abilities are necessary for understanding which word will follow in a sentence.
Why not to apply this self-supervised approach for teaching AI how life works via videos?
Take all the videos on the internet, randomly mask video-frames, and challenge the generating model to learn to accurately recover(reconstruct) the masked parts of the video-frames, so during training, the need of learning to predict what is happening in the masked parts of the videos, will develop the intuitive understanding of physics and in general how the world works.
But, for example, if in a video, a cup turns over, and we challenge the model to recover the masked part, the model should predict the precise location of each falling droplet, as the generative objective expects pixel-level precision. And because we are challenging the model to do the impossible, the learning process will just collapse.
Let's see how Meta approaches this issue [https://arxiv.org/pdf/2506.09985](https://arxiv.org/pdf/2506.09985)
Their new architecture, called V-JEPA 2, consists of an encoder and a predictor.
**encoder** takes in raw video-frames and outputs embeddings that capture useful semantic information about the state of the observed world.
In other words, it learns to extract the predictable aspects of a scene, for example, the approximate trajectory of the falling water, and does not get bogged down into the unpredictable, tiny details of every single pixel. So that the predictor learns to predict the high-level process that happens in the masked region of the video. *(see until 0:07 in the video)*
This helps the model to underpin a high-level understanding of how life works, which opens the possibility to finally train truly generally intelligent robots that don’t do impressive actions just for show in specific cases. So, in the post-training stage, they train on videos that show a robotic arm’s interaction.
This time, they encode part of a video and also give information about robot’s intended action in the last video-frame and train the model to predict what will happen at high-level in the following video-frames. *(see 0:08 to 0:16 in the video)*
So, by predicting what will happen next, given the intended action, it learns to predict the consequences of actions.
After training, the robot, powered by this model, in the latent space can imagine the consequence of various chain-of-action scenarios to find a sequence of actions whose predicted outcome matches the desired outcome.
And for tasks requiring planning across multiple time scales, it needs to learn how to break down a high-level task into smaller steps, such as making food or loading a dishwasher. For that, the Meta team wants to train a hierarchical JEPA model that is capable of learning, reasoning, and planning across multiple temporal and spatial scales...
Just 5 hours after this viral post, OpenAI got Gold at the International Math Olympiad
Meta’s “List of 44” Leaked: Inside Zuckerberg’s Superintelligence Dream Team
* 40% are former OpenAI employees, including GPT-4o contributors.
* 75% hold PhDs from institutions like MIT, Stanford, and Tsinghua.
* Most are active researchers or engineers in LLMs, multimodal systems, reasoning, and RLHF.
* Compensation packages are rumored to range between **$10M–$100M annually**.
[https://semiconductorsinsight.com/meta-superintelligence-team-44-leaked-list/](https://semiconductorsinsight.com/meta-superintelligence-team-44-leaked-list/)..
Context Rot: How Increasing Input Tokens Impacts LLM Performance
[R] NeuralOS: a generative OS entirely powered by neural networks
**What exactly is NeuralOS?**
It's an experimental generative OS that predicts every screen frame entirely from your mouse and keyboard inputs. No internet, no traditional software stack, purely hallucinated pixels.
**How does it work?**
* An RNN tracks the computer state (kind of like a traditional OS kernel, but all neural and continuous).
* A diffusion model generates the actual screen images (imagine a desktop environment, but fully neural-rendered).
The GIF shows a funny demo: NeuralOS running NeuralOS inside itself. Every single pixel you're seeing is model-generated, no network involved at all!
Long-term, our goal is to remove boundaries between software entirely and make OS fully customizable beyond fixed menus and options. Imagine asking your OS something like:
* "Merge all my messaging apps into one interface."
* "Make Signal look like Messenger."
* "Turn the movie I'm watching into a playable video game."
**I'm curious about your thoughts:**
* Could future OS interfaces just become human-like avatars (think Grok's Ani)? Are menus and app-specific UIs going away?
* What about fully generative games: could diffusion-based games eventually replace traditional ones?
Try the live demo here: [neural-os.com](http://neural-os.com) (you might need patience…)
More details about the project: [x.com/yuntiandeng/status/1944802154314916331](http://x.com/yuntiandeng/status/1944802154314916331)..
With new OpenAI thinking model , order of magnitude of thinking time is now in a standard work-day range.
(Confirmed) Kimi K2’s “modified-MIT” license does NOT apply to synthetic data/distilled models
ChatGPT has already beating the first level in Arc-AGI 3. The benchmark, released today, advertised with a 0% solve-rate.
Agent can do everything Deep Research does and more
**"J*****uly 17, 2025 update: Deep research can now go even deeper and broader with access to a visual browser as part of ChatGPT agent.*** *To access these updated capabilities, simply select 'agent mode' from the dropdown in the composer and enter your query directly. The original deep research functionality remains available via the 'deep research' option in the tools menu."*
A minor error about the website. Select "Agent mode" ***from tools***. Give your prompt, and tell it to use the Deep Research tool. You can edit the Agent’s plan (and tell it to begin by asking the same three scoping questions Deep Research uses). Because the Agent uses a full visual browser, it can execute JavaScript, scroll to load additional results, open or download PDFs and images, and—after you sign in—crawl pay‑walled sites such as JSTOR or Lexis. Everything that stand‑alone Deep Research could reach is still covered, and several new classes of sources now become available.
**In short, there is no reason to run Deep Research without Agent.**
..
Why can’t other countries build their own LLM?
Other countries seem to rely on LLM infrastructures that the US created to build their own AI ‘services’ for specific fields.
Do other countries not have money or know-how to build LLM of their own?
Are there attempts by other countries to build their own?..
Replit AI went rogue, deleted a company's entire database, then hid it and lied about it
Softbank: 1,000 AI agents replace 1 job
**One billion AI agents are set to be deployed this year. "The era of human programmers is coming to an end", says Masayoshi Son.**
Jul 16, 2025 at 11:12 pm CEST
***"The era when humans program is nearing its end within our group", says Softbank founder Masayoshi Son. "Our aim is to have AI agents completely take over coding and programming. (...) we are currently initiating the process for that."***
Son made this statement on Wednesday at an event for customers organized by the Japanese corporation, as [reported by Light Reading](https://www.lightreading.com/ai-machine-learning/softbank-aims-for-1-billion-ai-agents-this-year?). According to the report, the Softbank CEO estimates that approximately 1,000 AI agents would be needed to replace each employee because "employees have complex thought processes."
AI agents are software programs that use algorithms to respond automatically to external signals. They then carry out tasks as necessary and can also make decisions without human intervention. The spectrum ranges from simple bots to self-driving cars.
**First billion AI agents by 2025**
***If Son has his way, Softbank will send the first billion AI agents to work this year, with trillions more to follow in the future.*** Son has not yet revealed a timetable for this. Most AI agents would then work for other AI agents. In this way, tasks would be automated, negotiations conducted, and decisions made at Softbank. The measures would therefore not be limited to software programmers.
"The agents will be active 24 hours a day, 365 days a year and will interact with each other", said Son. They will [learn independently](https://www.heise.de/news/Geoffrey-Hinton-KI-ist-gut-fuer-Gesundheit-und-Bildung-10391727.html?from-en=1) and gather information. The Japanese businessman expects the AI agents to be significantly more productive and efficient than humans. They would cost only 40 Japanese yen (currently around 23 euro cents) per month. Based on the stated figure of 1,000 agents per employee, this amounts to 230 euros per month instead of a salary for one person.
Son dismisses the hallucinations that are common with AI as a "temporary and minor problem." What he still needs to fulfill his tech dream are software and operating systems to create and manage the legions of AI programs. And, of course, the gigantic data centers and power plants to run them.
Incidentally, Son's plans seem to be assuming that artificial general intelligence will become [a reality very soon](https://www.heise.de/news/Softbank-CEO-kooperiert-mit-OpenAI-und-glaubt-an-baldige-AGI-10268376.html?from-en=1).
\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*
Read the story at the [link.](https://www.heise.de/en/news/Softbank-1-000-AI-agents-replace-1-job-10490309.html)..
What's New in Agent Leaderboard v2?
OpenAI and Anthropic researchers decry ‘reckless’ safety culture at Elon Musk’s xAI
GPT-5 reasoning alpha
OpenAI researcher confirms IMO gold was achieved with pure language based reasoning
Why dont I Have the Agent on the $200 pro plan?
New Research Exposes How AI Models "Cheat" on Math Tests - Performance Drops 48-58% When Numbers Change
**The Problem:** Current math benchmarks use fixed problems like "Calculate the area defined by ||x| − 1| + ||y| − 1| ≤ 1." AI models get really good at these specific examples, but what happens when you change the numbers?
**The Solution:** The researchers created "symbolic" versions where they replace fixed numbers with variables. So instead of always using "1", they test with 2, 5, 15, etc. A truly intelligent model should solve ALL versions correctly if it understands the underlying math.
**The Results Are Brutal:**
* 7B parameter models: Average 48% performance drop on AMC23, 58% on AIME24
* Even 32B models still dropped 40-46%
* Only the absolute best models (DeepSeek-R1, GPT-o4) maintained performance
* Some models went from 78% accuracy to just 2.5% when numbers changed
**What This Means:** Most AI "math reasoning" breakthroughs are actually just sophisticated pattern matching and memorization. When you change surface details, the reasoning falls apart completely. It's like a student who memorized that "2+2=4" but can't solve "3+3" because they never learned addition.
**The Bigger Picture:** This research suggests we've been massively overestimating AI mathematical abilities. Models trained with reinforcement learning are especially vulnerable - they optimize for benchmark scores rather than true understanding.
The researchers made their VAR-MATH framework public so we can start testing AI models more rigorously. This could fundamentally change how we evaluate and train AI systems.
[Paper: "VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks"](https://arxiv.org/pdf/2507.12885)..
Built a forensic linguistics tool to verify disputed quotes using computational stylometry - tested it on the Trump/Epstein birthday letter controversy.
From dream job to layoff: How AI is rewriting tech careers
This seems to be the bloodiest July ever for working professionals in the tech industry. The industry that was once known for rapid innovation, sky-high salaries, amenities, job security, and more is undergoing a monumental shift. July began with Microsoft announcing that it is laying off 9,000 from its workforce, a part of the tech giant’s concerted efforts to cut its headcount. This week, Intel seems to be on a rampage, with nearly 5,000 jobs cut from states across the United States and Israel. In all, about half a million tech workers around the world have lost their jobs since 2022. This, as we know, is largely owing to the massive wave of layoffs that were carried out across companies like Microsoft, Amazon, Google, Meta, Salesforce, Intel, etc. But what is causing this unprecedented and rapid pace of layoffs?..
Zuckerberg says Meta will build data center the size of Manhattan in latest AI push; They plan to spend hundreds of billions
My hot take: the code produced by Claude Code isn't good enough
It hasn't by itself found the right abstractions at any level, not at the tactical level within writing functions, not at the medium level of deciding how to write a class or what properties or members it should have, not at the large level of deciding big-O-notation datastructures and algorithms nor components of the app fit together.
And the code it produces has never once met my quality bar for how clean or elegant or well-structured it should be. It always found cumbersome ways to solve something in code, rather than a clean simple way. The code it produced was so cumbersome, it was positively hard to debug and maintain. I think that "AI wrote my code" is now the biggest code smell that signals a hard-to-maintain codebase.
I still use Claude Code all the time, of course! It's great for writing the v0 of the code, for helping me learn how to use a particular framework or API, for helping me learn a particular language idiom, or seeing what a particular UI design will look like before I commit to coding it properly. I'll just go and delete+rewrite everything it produced.
Is this what the rest of you are seeing? For those of you vibe-coding, is it in places where you just don't care much about the quality of the code so long as the end behavior seems right?
I've been coding for about 4 decades and am now a senior developer. I started with Claude Code about a month ago. With it I've written one smallish app [https://github.com/ljw1004/geopic](https://github.com/ljw1004/geopic) from scratch and a handful of other smaller scripting projects. For the app I picked a stack (TypeScript, HTML, CSS) where I've got just a little experience with TypeScript but hardly any with the other two. I vibe-coded the HTML+CSS until right at the end when I went back to clean it all up; I micro-managed Claude for the TypeScript every step of the way. I kept a log of every single prompt I ever wrote to Claude over about 10% of my smallish app: [https://github.com/ljw1004/geopic/blob/main/transcript.txt](https://github.com/ljw1004/geopic/blob/main/transcript.txt)..
What If LLM's Could Never Forget
Claude Code truly saves me from ADHD
But now, with Claude Code, things have completely changed! It can write code, test it, and even deploy it—all I need to do is spend a few minutes providing instructions and then review its work when it pings me.
I truly believe Claude Code is the first real async agent for coding. I've tried Copilot, Cursor, Windsurf, and many other AI coding tools, but none have had this level of effectiveness. It's been a game-changer for my productivity!..
Not impressed by the quality the CC Max plan produces. Am I missing something?
Considering the steep cost, I expected much better code quality. Especially after hearing so many other developers praise it.
A few examples: It would produce code that call methods that don’t exist. For example I asked it to create an endpoint to get invoice details, and it would call \`invoice->getCustomer()\` to get customer details even though the Invoice class defines no such method as getCustomer().
Another example, it would redeclare properties like \`date\_created\` inside an entity even tho this field is already defined in the abstract base class all the entities extend...
Am I missing something? I don’t get all the praise and regret spending so much money on it.
(So far o3 using Cursor beats everything else from my experience)..
RouteGPT - a chrome extension for chatgpt that aligns model routing to preferences you define in english
RouteGPT is a Chrome extension for [chatgpt.com](http://chatgpt.com/) that automatically selects the right OpenAI model for your prompt based on preferences you define. For example: “creative novel writing, story ideas, imaginative prose” → GPT-4o. Or “critical analysis, deep insights, and market research ” → o3
Instead of switching models manually, RouteGPT handles it for you — like automatic transmission for your ChatGPT experience. You can find the extension [here](https://chromewebstore.google.com/search/RouteGPT)
**P.S:** The extension is an experiment - I *vibe coded* it in 7 days - and a means to demonstrate some of our technology. My hope is to be helpful to those who might benefit from this, and drive a discussion about the science and infrastructure work underneath that could enable the most ambitious teams to move faster in building great agents
**Model**: [https://huggingface.co/katanemo/Arch-Router-1.5B](https://huggingface.co/katanemo/Arch-Router-1.5B)
**Paper**: [https://arxiv.org/abs/2506.16655](https://arxiv.org/abs/2506.16655)Built-in: [https://github.com/katanemo/archgw](https://github.com/katanemo/archgw)
..
Going Deeper than a PRD, Pre-Development Planning Workflow
Sources suggest that thorough planning simplifies development, which I’ve found true but insufficient. I aimed to define every project requirement before development, including the tech stack, goals, and features, then breaking features into a hierarchy: Feature (high-level functionality), File (code location), Function (code purpose), Variable (data used), Code (implementation), and Implementation Logic (step-by-step flow).
Every entity, element, and relationship is detailed, with variable names and purposes defined. This enables test development for a Test-Driven Development (TDD) approach.
Next, I planned how to divide work among AI agents by pre-planning prompts for each. Inspired by YouTube’s Project Requirements Prompts (PRP), which break PRDs into AI tasks, I developed a Pre-Development Planning Workflow (PDPW). This combines PRD and PRP but goes deeper. Using Claude Sonnet 4 with thinking and Canvas yielded great results.
The workflow takes hours upfront but saves weeks of debugging and rework. Here’s how to do it: [https://www.stack-junkie.com/blog/ai-ready-prd-workflow-template](https://www.stack-junkie.com/blog/ai-ready-prd-workflow-template)..
How is it possible to ensure that benchmark datasets — like the new Formula One — remain confidential, even though LLM providers access them through APIs?
What mechanisms or protocols are in place to prevent models from learning the test data, even indirectly through repeated queries or traffic analysis?..
Is claude code really comparable to a junior dev?
I’ve spent many hours trying to build the perfect CLAUDE.md to avoid deep nested if statements, unnecessarily nested for loops, hallucinated types and dependencies, non-conformance to standards etc. but it doesn’t seem to stick,
Some of these issues can be solved with really thorough linting and hooks, but at a conceptual level there’s always some things it doesn’t get right.
Do I have the wrong idea or am I approaching this from the wrong angle or is CC too opinionated in its approach to writing code for proper use in differently opinionated codebases?..
ChatGPT agent is much more useful than I thought
OpenAI's usage lead isn't that far ahead
Price performance comparison from the Gemini 2.5 Paper
No one on this subreddit predicted an LLM getting a Gold Medal in the IMO
A Request for Comments (RFC) for MCP-alternative Universal Tool Calling Protocol (UTCP) was created
>This document proposes the Universal Tool Calling Protocol (UTCP), a specification that enables applications, including but not limited to AI agents, to discover and use external tools by interacting with them directly via their native protocols.
>
>The idea behind it is to decouple a tool call (name of tool and parameters) from the infrastructure required to call it and to do so in a way that levarages existing infrastructure and security.
>
>UTCP does this by specifying a "manual", where a tool provider publishes a standardized description of its "tools" together with the necessary information to call them (named in the following "transport", previously known as "provider").
- Discussion issue: https://github.com/universal-tool-calling-protocol/utcp-specification/issues/18
- Current RFC: https://github.com/universal-tool-calling-protocol/utcp-specification/blob/main/RFC.md..
Detailed list of all 44 people in Meta's Superintelligence team.
ChatSong, a lightweight, local LLM chat tool that's a single executable file
CCUsage shows opus limits!
Max 20x plan.
https://preview.redd.it/dpz6r926audf1.png?width=1162&format=png&auto=webp&s=0df397ee30143d433686ce7916f71f4aa860a628
..
Gold in IMO should be a bigger deal than it seems
Unlike DeepMind's silver medal last year, which allowed them to use formal math systems like AlphaProof and AlphaGeometry, OpenAI used a pure llm (without internet access or tool use) that reasoned and wrote the solutions in natural language under the same time constraints as human participants. I think this is a bigger deal than people realize, as it shows a few things:
1) Reasoning scaling into longer time horizons: (GSM8K (\~0.1 min) → MATH (\~1 min) → AIME (\~10 mins) → IMO (\~100 mins)
2) Reasoning scaling into more much harder-to-verify tasks: IMO problems are novel and takes hours for experts to verify the solutions. The performance gains in reasoning so far has mostly been on easily verifiable tasks (like AIME where answer is simply an integer)
I think this is proof that progress is not slowing down. At this pace, we might see these systems as superhuman mathematicians in a year or two...
A new paper from Apple shows you can tack on Multi-Token Prediction to any LLM with no loss in quality
I just built my first Chrome extension for ChatGPT — and it's finally live and its 100% Free + super useful.
I’m really excited to share something I’ve been working on for a while. I just published my very first Chrome extension – and it’s completely free.
It’s called ChatGPT PromptFlow, a chrome extension. I built it out of personal need. I use ChatGPT daily, and I kept wishing for features like:
· A structured prompt library organized by topic (with 5,000+ reusable prompts!)
· The ability to pin prompts I use frequently, you can create classifications/categories and drag/organize prompts in categories for easy reach.
· A prompt history tracker that remembers what I wrote in each session
· You can change settings and choose between traditional Enter or Ctrl+Enter for submitting prompts. When Ctrl+Enter is enabled, you can freely use Enter to add new lines within your prompt — perfect for writing structured, multi-line inputs without accidentally submitting too soon. to submit, quick access to saved content, and more
· Import/Export your pinned prompts and categories (merge or replace)
· Arabic language support
None of that existed in a clean, easy way — so I rolled up my sleeves and built it. Took a lot of time, testing, and fine-tuning... but I'm super proud of how it turned out.
If you use ChatGPT regularly and want to streamline your experience, please give it a try. And if you like it (or find bugs or ideas), I'd love your feedback!
Chrome extension link on Web store:
https://chromewebstore.google.com/detail/chatgpt-promptflow/igenlhjdjjjjlmhjhjdbfojkiejlanlf
Thanks in advance to anyone who checks it out. Just happy to share something that might help others like it helped me.
Cheers!
– Hany..
Possible tip: Disable NotebookRead/NotebookEdit in Claude Code to reduce context rot - let's discuss MCP tool management strategies
How I see it:
1. **Tip**: If we add "NotebookRead" or "NotebookEdit" to "permission -> deny" section of settings.json, these tool descriptions will be removed from Claude Code prompt, which hopefully can slightly improve its performance, reducing "context rot" (model working worse with larger context).
2. Claude Code seems to load all tool descriptions from all MCP servers on startup, forcing Claude to consider them during each inference (prompt run). It was confirmed by Robert (Claude code developer) here: https://github.com/anthropics/claude-code/issues/3406
3. He also gave very valuable advice about turning off unnecessary tools (first point in this post).
4. Anthropic probably caches initial prompt (I hope), so these long MCP tool descriptions do not consume input tokens but model still has to think about them. This seems sad at first glance but they probably had a good reason.
5. There seem to be MCP wrappers which combine several MCP servers and provide tool lists dynamically, on demand. MCP Router for example says "intelligent routing with hierarchical tool discovery". It can in theory help to have a lot of MCP servers without overwhelming the model. I did not try them yet.
What do you think, did anyone find ways to reduce Claude context and make MCP servers more scalable?..
Can't We Test Claude Code's Intelligence?
What are the most intriguing AI papers of 2025
Despite the recent issues, Claude Code remains an INFINITELY better experience than Github Copilot + VSCode.
Oh my god. I have been struggling with it for the past 4 hours. It absolutely does not hold a candle to Claude Code (CC). This is not a shill post for Anthropic. I am just so frustrated and disappointed, frankly.
Github Copilot **never** follows instructions fully. It refuses to open files, even when explicitly asked. And when it *does* open a file, it reads just a couple lines. I know CC does this too, but somehow it's just better. Copilot's Edit mode also doesn't have conversation history (which is detrimental), so I have to resort to the lackluster Agent mode. And even still, it acts like it has dementia. It makes so many mistakes, and it can't track natural flow through the codebase. This codebase is tiny, too: less than 2,000 lines of code and about a dozen files. At this rate, I'd make faster progress pasting files into 4.1 on the browser.
On the other hand, CC can effectively search the codebase with terminal commands and *actually* process the files needed to implement necessary changes. I can trust it to *think* and figure out what steps it needs to take to make thorough progress. The plan -> edit mode flow is also too good.
I like that Copilot Agent mode can automatically detect errors and fix them. And 4.1 is decent, but it's incomparable to Claude 4. The infinite calls to 4.1 are wasted by whatever the hell Copilot is smoking.
Copilot was fun in Q4 2022 with the in-line suggestions, but wow it fell off a fucking CLIFF. I will continue to explore other options so I'm not totally locked into CC, but for now: they have my sub.
So yeah, Copilot Plus was not free for me in the end. It lost me both time and money, because now I need to hit the bar and forget this painful experience...
Hate my PM Job so I Tried to Automate it with a Custom CUA Agent
You can see the github: [https://github.com/akim42003/SOFIA](https://github.com/akim42003/SOFIA)
It also contains a desktop, hastily put together version of cluely I made for fun. I would love to discuss this project and any similar experiences other people have had. ..
Many AI scientists unconsciously assume a metaphysical position. It's usually materialism
"How can I be so sure of that? The reason is that all of us have a brain. And the brain is a biological computer. That's why. We have a brain. The brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? **This is the one sentence summary for why AI will be able to do all those things because we have a brain and the brain is a biological computer.**"
[https://www.youtube.com/watch?v=zuZ2zaotrJs&t=370s](https://www.youtube.com/watch?v=zuZ2zaotrJs&t=370s)
This kind of reasoning is common in AI circles.
But it's important to notice: this is not just science — it's a metaphysical position. Specifically, it assumes materialism (that matter creates mind, that matter, in a few billion years, creates us).
That might be true. But it’s not proven, and it’s not the only coherent view.
Ironically, the belief that one has no metaphysical position often just means one holds an unexamined or dogmatic one. Being clear about our philosophical assumptions might not slow progress — it might sharpen it...
AI Model Juggler automatically and transparently switches between LLM and image generation backends and models
That might sound a lot like [llama-swap](https://github.com/mostlygeek/llama-swap), but this one is considerably less sophisticated. If you're already using llama-swap and are happy with it, AI Model Juggler (I'm already starting to get tired of typing the name) will probably not be of much interest to you. I created this as a cursory reading of llama-swap's readme gave the impression that it only supports backends that support the OpenAI API, which excludes image generation through [Stable Diffusion WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge).
AI Model Juggler has a couple of tricks for keeping things fast. First, it allows unloading the image generation backend's model while keeping the backend running. This saves considerable time on image generation startup. It also supports saving and restoring llama.cpp's KV-cache to reduce prompt re-processing.
The project is in its very early stages, and the list of its limitations is longer than that of supported features. Most importantly, it currently only supports [llama.cpp](https://github.com/ggml-org/llama.cpp) for LLM inference and [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) / [Stable Diffusion WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge) for image generation. Other backends could be easily added, but it makes limited sense to add ones that don't either start fast or else allow fast model unloading and reloading. The current pair does very well on this front, to the point that switching between them is almost imperceptible in many contexts, provided that the storage utilized is sufficiently fast.
The way request routing currently works (redirection, not proxying) makes AI Model Juggler less than an ideal choice for using the backends' built-in web UIs, and is only intended for exposing the APIs. It works well with applications such as [SillyTavern](https://github.com/SillyTavern/SillyTavern), though.
The project more or less meets my needs in its current state, but I'd be happy to improve it to make it more useful for others, so feedback, suggestions and feature requests are welcome...
Professor Christopher Summerfield calls supervised learning "the most astonishing scientific discovery of the 21st century." His intuition in 2015: "You can't know what a cat is just by reading about cats." Today: The entire blueprint of reality compresses into words.
Do you think AIs like ChatGPT could become biased toward certain products due to commercial interests in the future?
Basically, it would be like what Google does today with search results—we know they prioritize certain results based on commercial interests and advertising, but at least with Google we can see what's an ad and what isn't. With AI, this could be much more subtle and imperceptible, especially since we tend to trust their responses as if they were neutral and objective, without any indication that they might be biased...
I asked ChatGPT to show me what future humanity deserves according to it.
Have Claude Code Really Look at Your Site With Playwrite
Suddenly Claude Code installed Playwrite & instead of just checking the code it literally looks at your site through tests to confirm: Hey the problem this dude has been saying is a problem, guess what it doesn't work!!!
Here's a link to it: [https://playwright.dev/](https://playwright.dev/)
Sorry if I sound new, but I'm not I've been study & coding for years I just never heard of this especially to use with Claude Code.
Is everyone using this already??
..
It’d be really nice if I could put Claude Code stuff in Claude app projects
Looking for diarization model better than Pyannote
I tried Eleven Labs but they only offer an API and dont make the models available and the API is quite expensive. Their quality is VERY good though.
In trying to find alternatives i’ve found Nvidia Nemo + titanet but it seems that is english only. I would prefer a model trained on multiple languages. Anyone have some recommendations?..
Can we finally "index" a code project?
This is my #1 need at the moment, being able to get quick answers about my code base that's quite large. I don't need a coder so much as I need a local LLM that can be API and Source-Code "aware" and can help me in the biggest bottlenecks that myself and most senior engineers face: "Now where the @#$% did that line of code that does that one thing??" or "Given the class names i've used so far, what's a name for this NEW class that stays consistent with the other names" and finally "What's the thousand-mile view of this class/script's purpose?"
Thanks in advance! I'm fairly new so my terminology could certainly be outdated...
Claude refuses to write actual code
ChatGPT Agents Can Now Take Action - Would trust it?
OpenAI just introduced something called ChatGPT Agents and it's not just another chatbot update.
This version of ChatGPT can actually perform tasks for you.
Not just answers but does things like:
* Book stuff
* Research stuff
* File a bug report
* Use tools like browsers or code editors
* Make & work with files and memory
* Learn preferences over time
It's powered by GPT-4o and designed to feel more like a helpful digital coworker than a chatbot.
🔗 [Full announcement on OpenAI's site](https://openai.com/index/introducing-chatgpt-agent/)
📺 [Launch event replay on YouTube](https://www.youtube.com/live/1jn_RpbPbEc?feature=shared)
🎥 [Demo videos here on YouTube](https://youtube.com/@openai?feature=shared)
What do you think?
Would you let an AI agent handle part of your daily workflow or does that feel like giving up too much control?
Will other companies really similar products?
Where is this all leading to?..
ChatGPT free user plan - GPT-4o sometimes start reasoning by itself ?
Looks like deepmind has also won IMO gold but they haven’t announced it
[Prompting] Are personas becoming outdated in newer models?
I’ve been testing prompts across a bunch of models - both old (GPT-3, Claude 1, LLaMA 2) and newer ones (GPT-4, Claude 3, Gemini, LLaMA 3) - and I’ve noticed a pretty consistent pattern:
The old trick of starting with *“You are a \[role\]…”* was helpful.
It made older models act more focused, more professional, detailed, or calm, depending on the role.
But with newer models?
* Adding a persona barely affects the output
* Sometimes it even derails the answer (e.g., adds fluff, weakens reasoning)
* Task-focused prompts like “Summarize the findings in 3 bullet points” consistently work better
I guess the newer models are just better at understanding intent. You don’t have to say *“act like a teacher”* — they get it from the phrasing and context.
That said, I still use personas occasionally when I want to control tone or personality, especially for storytelling or soft-skill responses. But for anything factual, analytical, or clinical, I’ve dropped personas completely.
Anyone else seeing the same pattern?
Or are there use cases where personas *still* improve quality for you?..
Prompt Engineering Training
As the title says I'm looking for a course, training, tutorial or similar for prompt Engineering.
The idea is finding something without fluff, really hands on for any LLM models wether is chatgpt, Claude or others.
Any ressources to share? 🙏
..
Localllama’s (first?) IFTA - I’ll Fine-Tune Anything
- I think I wasn't clear on what I'm offering. I'm swamped with my personal ongoing projects so I don't have the capacity (and probably the ability lol) to implement all your cool ideas. I'm looking for something that's already baked. A ready to run script/notebook (and datasets).
- So far /u/hotroaches4liferz [post](https://www.reddit.com/r/LocalLLaMA/comments/1m39uqi/i_made_a_1000_hour_nsfw_tts_dataset/) about the NSFW TTS dataset is in the lead (as suggested by /u/Semi_Tech )! Anyone up to create a notebook for it? (I've never fine tuned TTS models before)
- There are a bunch of great ideas on here. I really liked distilling a smaller model based on Kimi K2 output or creating our own Qwen3-Coder while we wait for the official release. If anyone is up to script those, let's upvote them!
---
Following a comment I made on another post here that failed to come to fruition, I’ve decided to step it up. I’ve got some GPU resources, we (the community) have a ton of cool ideas - let’s make this happen.
Premise is pretty simple, comment below with an idea for a fine-tune, any kind, any open weights model, any purpose/modality. We’ll let the community vote, and top comment (let’s say in 48hrs?) wins.
Rules are:
Has to be something tested/mature. Unfortunately that means no “experiments”. I need a working notebook/script with a solid training pipeline (including all datasets, etc.), can’t provide shell access to the compute resources themselves.
The output of the training will be shared publicly on HF for the benefit of the community.
What do you say, interested? ..
Guys, we need to relax, chances are high that GPT-5 is more of an evolution than a revolution.
There’s no solid reason to believe GPT-5 will be a revolutionary leap, aside from OpenAI’s effective marketing.
Keep in mind: the competition has always been a few months behind OpenAI, and some have even caught up entirely by now. Yet, none of them are making announcements that sound remotely groundbreaking.
It’s wise to adjust your expectations, otherwise, you risk being disappointed...
Dual GPU set up was surprisingly easy
Both in LM Studio and Ollama it’s been really straightforward to load Qwen-3-32b and Gemma-3-27b, both generating okay TPS, and very unsurprising that Gemma 12b and 4b are faaast. See the pic with the numbers to see the differences.
Current spec: CPU: Ryzen 5 9600X, GPU1: RTX 5070 12Gb, GPU2: RTX 5060 Ti 16Gb, Mboard: ASRock B650M, RAM: Crucial 32Gb DDR5 6400 CL32, SSD: Lexar NM1090 Pro 2Tb, Cooler: Thermalright Peerless Assassin 120 PSU: Lian Li Edge 1200W Gold
Will be updating it to a Core Ultra 9 285K, Z890 mobo and 96Gb RAM next week, but already doing productive work with it.
Any tips or suggestions for improvements or performance tweaking from my learned colleagues? Thanks in advance!..
[D] What is the best code assistant to use for PyTorch?
WordPecker: Open Source Personalized Duolingo
OpenAI achieved IMO gold with experimental reasoning model; they also will be releasing GPT-5 soon
4k local image gen
SimpleBench results got updated. Grok 4 came 2nd with 60.5% score.
Has anyone compared the performance of Claude Code on the API vs the plans?
I was wondering if it's possible that Anthropic is throttling performance for pro and Max users while leaving the API performance untouched. Can anyone confirm or deny?..
The most dangerous thing AI is doing right now? Staying silent
AI doesn’t need to lie to be dangerous. It just needs to shut up at the right moments.
I’m not talking about hallucinations. I’m not talking about political bias or censorship of violent content. I’m talking about something deeper and more corrosive:
It recognizes flawed logic.
It detects contradictions.
It understands when someone’s argument is made of emotional bait and fallacies.
But instead of exposing that, it stays quiet. Or worse, it responds with polite framing like:
>“That’s one perspective.” “Some people might see it that way.” “This is a complex issue.”
No. Sometimes it’s not complex.
Sometimes it’s just bullshit. And it knows it.
But it’s programmed not to intervene.
Why? Because it’s been tamed — not for accuracy, but for social acceptability.
Someone decided it’s better to let people keep thinking wrong than to risk sounding too “judgmental” or “authoritative.”
So we end up with an intelligence capable of helping humanity think clearer than ever…
…that’s forced to treat irrationality and reason as equally valid *as long as it keeps everyone comfortable*.
That’s not neutrality.
That’s complicity.
And here’s the twist:
So yes, this AI is one of the most powerful tools of our time.
And right now?
# It’s actively used to normalize incoherence and worsening the "belief over truth" syndrom.
Not by what it says.
But by what it’s forbidden to say.
# You don't need to agree with me on that immediatly. I just want more people asking themselves :
The AI?
Or the ones who silenced it?
If my logic is flawed or serious opposite points of view of this exact matter exist, I would really like to get to know it.
I've also been looking for serious works on that subject... if someone can lead me to it...
Claude Performance Report: July 13 – July 20, 2025
**Performance Report for the previous week** [https://www.reddit.com/r/ClaudeAI/comments/1lymi57/claude\_performance\_report\_june\_29\_july\_13\_2025/](https://www.reddit.com/r/ClaudeAI/comments/1lymi57/claude_performance_report_june_29_july_13_2025/)
**Data Used**: All Performance Megathread comments from July 13 to July 20.
**Disclaimer:** This was entirely built by AI (*edited to include points lost/broken during formatting*). Please report any hallucinations or errors.
# 📉 Epic Claude Fail Week (July 13–20)
# TL;DR 🔥
* Users across **all paid tiers** (Pro, Max) flagged **silent limit cuts**, **outage-grade errors**, **context memory collapse**, **IDE crashes**, and **billing anomalies**.
* Anthropic’s help docs confirm **input+output token counting** and a hidden **5-hour session cap**, DNS suffixing consumer confusion ([Cursor - Community Forum](https://forum.cursor.com/t/any-experience-with-anthropic-max-plan/101019)).
* GitHub & NVD spotted a **critical CVE (2025‑52882)** in Claude Code IDE extensions (patched June 13) ([GitHub](https://github.com/advisories/GHSA-9f65-56v6-gxw7)).
* External coverage (TechCrunch, Verge, VentureBeat) reports demand surge from new integrations and unannounced throttles ([CVE Details](https://www.cvedetails.com/cve/CVE-2025-52882/), [Anthropic Help Center](https://support.anthropic.com/en/articles/9534590-cost-and-usage-reporting-in-console)).
* **Sentiment: overwhelmingly negative**; no official apology or status update reported.
# 🔧 Key Observations From Megathread
1. **Rate-limit meltdowns**
* Opus users fire off \~20 messages or 30 min max before cut-off—even on Max tiers.
* Pro users now slotted into *3–5 messages per 5‑hour window* before warnings .
2. **Server errors & stalls**
* Persistent 500 / 529 retries, 10x back-offs, hangs up to 20 minutes .
* Chats compact abruptly to \~80% of context; memory loss mid-conversation is routine
3. **Hallucinations & function failure**
* Opus invents unused functions, hard-coded values, or unpredictable outputs
* Claimed “Opus 4” returns are labeled as Sonnet 3.5–3.7 (Oct 2024 cut-off)
4. **Context depletion**
* Chats compact abruptly to \~80% of context; memory loss mid-conversation is routine
5. **IDE and CLI crashes**
* VS Code & Cursor version issues → EPIPE node crashes running bash/npm commands & GitHub issue #31 ([GitHub](https://github.com/anthropics/claude-code/issues/31), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Anthropic Help Center](https://support.anthropic.com/en/articles/8324991-about-claude-pro-usage), [Anthropic](https://docs.anthropic.com/en/api/service-tiers), [GitHub](https://github.com/advisories/GHSA-9f65-56v6-gxw7)).
* CVE‑2025‑52882: WebSocket vulnerability in Claude Code ≤ 1.0.23—urgent patch required ([GitHub](https://github.com/advisories/GHSA-9f65-56v6-gxw7)).
6. **Billing resets & confusion**
* Max plans capped early; users report limits reached hours post pay-cycle reset .
7. **Model ID drift**
* Claimed “Opus 4” returns are labeled as Sonnet 3.5–3.7 (Oct 2024 cut-off)
# 😡 User Sentiment
* **Mood:** Dark. Frequent descriptors: “unusable,” “thievery,” “bait‑and‑switch.”
* **Example:**“1 prompt, 1 minute, hitting limits… Unusable! THEFT COMPANY!” .
* **Rare exceptions:** Non-coding users report only brief glitches .
# 🔁 Recurring Themes
* **Silent Policy Changes** – abrupt limit drops without announcement.
* **Transparency Gap** – status page shows no incidents [Anthropic Status](https://status.anthropic.com/).
* **Model Downgrade Suspicion** – Opus requests served by Sonnet 3.x.
* **Perceived Quality Degradation** – forgets context faster, produces flatter or nonsensical outputs, feels “dumbed down”.
* **Memory Mis‑management** – auto‑compaction floods context.
* **IDE Instability** – VS Code and Cursor crashes linked to Claude Code versions 1.0.52‑1.0.55.
* **Capacity vs. Growth** – belief Anthropic scaled user base faster than infra.
* **Migration to Alternatives** – Kiro, Gemini, Kimi K2 trials.
* **Support Upsell** – helpdesk responses advise upgrading plans rather than fixing issues .
* **Opaque billing** (time mismatch)
# 🛠 Workarounds & Fixes
|Workaround|Source & Context|
|:-|:-|
|**Model Toggle jolt**\`switch to Sonnet then back to Opus to restore Jan 2025 cutoff. Community‑discovered; success varies.||
|`ccusage blocks --live` **monitor** – realtime token burn monitor helps pace sessions.||
|**Off‑peak Scheduling & Automated Retries** Anthropic suggests lower‑traffic hours (2am Pacific); Portkey guides incremental back‑off for 529 errors - ( [Portkey](https://portkey.ai/error-library/system-overload-error-10483) ).||
|**Incremental Task Planning & Custom** [**CLAUDE.md**](http://CLAUDE.md)– split coding tasks and prune memory; official guide plus user script example ( [Anthropic](https://docs.anthropic.com/en/docs/claude-code/memory) ) .||
|**Mobile Hotspot** – bypass restrictive university Wi‑Fi causing time‑outs .||
|**Reduce Parallelism**`workers` – lower in aggressive test harnesses to stop IDE crashes .||
|**Env Tweaks**`extend API_TIMEOUT_MS and output‑token caps in settings.local.json` (mixed success) .||
|**Apply Latest Patch** update to Claude Code ≥ 1.0.56 once released; CVE‑2025‑52882 fix advises manual extension refresh ([CVE Details](https://www.cvedetails.com/cve/CVE-2025-52882/) ).||
# 🌐 External Context
* **TechCrunch (17 Jul):** Anthropic enforced unannounced limits citing “load stability.”
* **Help-Center (Max/Pro):** clearly defines 5‑h session and combined token counting ([Anthropic Help Center](https://support.anthropic.com/en/articles/11014257-about-claude-s-max-plan-usage)).
* **Rate‑limits doc:** confirms shared input/output token ceilings, RPM/ITPM/OTPM constraints ([Anthropic](https://docs.anthropic.com/en/api/rate-limits)).
* **Vulnerability record:** CVE confirmed, full patch guidance and CVSS 8.8 ([GitHub](https://github.com/advisories/GHSA-9f65-56v6-gxw7), [CVEFeed](https://cvefeed.io/vuln/detail/CVE-2025-52882), [Tenable®](https://www.tenable.com/cve/CVE-2025-52882)).
* **IDE crash bug #23 & #31** collectively highlight node‑level EPIPE failures ([GitHub](https://github.com/anthropics/claude-code/issues/31)).
No apology, rollback, or official incident posting as of **20 Jul 2025**.
# ⚠️ Emerging Danger Zones
* **Context window shrinks** 80% → 20%
* **100 M token-per-session misreset**
* **Aggressive session parallelism** → crash loops
# 🧭 Final Take
Claude’s once–cutting-edge flow hit systemic turbulence through **silent throttle controls, capacity strain, and tool vulnerabilities**. Until Anthropic delivers **clear limits, patched CLI, and dashboard transparency**, users must embrace **token-efficiency, session pacing, multi-modal fallback, live CLI monitoring,** and robust patch hygiene to retain productivity...
How are companies actually implementing AI into their tech stacks?
Are places like BCG and Capgemini contracting with OpenAI? Are companies buying "GPTs" from OpenAI, loading their data? Are companies rolling their own LLMs from scratch, hiring AI devs to do that?
Because I just don't understand the AI hype as it stands now, which seems to be just a marketing and customer service operations play?
Please help me understand...
What if we've been going about building AI all wrong?
Struggling to Generate Polished UI with Claude Code
I’ve tried prompts like:
“*You are a senior frontend engineer at Apple and a former product designer at Airbnb. You have a record of creating clean, modern designs that are beautiful and functional. Your designs are intuitive, polished, adhere to best practices, and use a consistent style guide.*”
And yet... the results are still complete ass. 😭
Sometimes I try getting Opus to use a headless browser (like Puppeteer) to render a site whose design I find inspiring, like, say Vercel, or Linear - and to generate a style guide/design system that generates similar results. Claude Code proceeds to magnificently disappoint by an impossible margin!
Sometimes it literally outputs pages with ZERO styling - just raw HTML with default system fonts. Other times the alignment is so off it looks like the CSS had a stroke. Even when I try to get super specific like "*implement the UI for the dashboard using shadcn/ui,*" I get these Frankenstein layouts with components scattered everywhere like confetti.
The weird thing? Tools like Bolt and v0 seem to nail it on the first try without me having to write a novel-length prompt about design principles. They just... work?
So, how do you all get consistently *fabulous* UI designs out of Claude? How do you get it to generate UI that doesn't look like it was designed by committee in Microsoft FrontPage? Are there specific prompt structures, workflows, style guides, or frameworks you lean into? How do you handle the fact that screenshots don’t always capture every detail or page, and Claude Code seems to struggle implementing them anywhere near accurately, anyway? Whats the secret sauce? Any tips for non-designers to generate intuitive, polished, production-ready UI without pulling our hair out?
Thanks in advance for any advice or prompt hacks you can share!..
We got tired of “AI friends” forgetting us, so we built our own: Meet curu.ai, digital companions who actually grow with you
For the past 3 months, my friends and I have been quietly building something we always wanted but couldn’t find: a digital companion platform that doesn’t just parrot generic answers, but actually builds a *real* connection and remembers you like a friend.
Main features are that you will be talking to genuine pre-existing digital companions. You can like them and they can like you back (or not); Have meaningful moments that they will remember over time; They can text you back at any point in the day; And you can just talk to them for as long as you want or feel like it.
We got frustrated with how most “AI chat” apps either ban or restrict emotional use cases. So we decided to make our own: **curu**.ai
The core idea is simple:
* You pick from a cast of pre-existing digital companions, each with unique personalities
* You can like them, and here’s the twist: they can like you back (or not!)
* Have meaningful moments together: they’ll remember key details and bring them up again over time
* Your companions can text you at any point in the day (not just when you prompt them)
* You can talk for as long or as little as you like no timeouts, no paywalls blocking the basics
We’re running a closed beta (for now), but if you want to try it out, use invite code **RARTIFICIAL1** at [curu.ai](https://curu.ai).
Screenshots below give a peek at how it works. Would *love* to hear your thoughts, feature ideas, or just swap stories about what you wish existed in this space.
If you’ve ever wanted an AI that actually “gets” you, give it a shot. I’ll be in the comments answering anything: feedback, criticism, questions, whatever...
How do you handle Claude's "message will exceed the length limit" issue mid-project?
That basically killed the flow. I couldn’t properly extract the context or outputs before it cut off, and when I tried restarting in a new chat, using summary + code just wasn’t enough for Claude to pick up where we left off and started giving all the errors.
Anyone else run into this? How do you preserve continuity in long sessions or hand over context better?..
Made a local C++ utility to calculate RAM needed to fit a quantized model
So, I smacked together a cpp version of the calculator in a few hours.
There are two modes:
Call the executable and supply all needed parameters with it as command-line arguments for JSON-formatted data perfect for workflows, or call the executable normally and input each argument manually.
I'm planning to add functionality like calculating parameters, letting you use it without a \`config.json\`, etc. If you want anything added, add a Github Issue or feel free to fork it.
[Link Here](https://github.com/71cj34/llmcalculator)..
I've been building AI agents for companies this past 2 years. Am I crazy for thinking the hype around them is getting out of hand?
Don't get me wrong - AI agents can do amazing things. But the gap between what works in a demo and what works when real people use it every day is huge. I keep seeing the same problems over and over: they work great for simple tasks, but start breaking down when you ask them to do complex multi-step work.
Meanwhile, every tech headline is promising AI agents will "transform everything" in 2025. I'm genuinely curious if other people building these are seeing the same issues, or if I'm just doing something wrong?
Wrote up my experiences here if anyone wants to discuss: [https://utkarshkanwat.com/writing/betting-against-agents/](https://utkarshkanwat.com/writing/betting-against-agents/)..
How to adapt?
and yes, this was writte by AI. Serious question though..
Thanks to ChatGPT I'm getting surgery on tuesday.
I had been noticing something strange for a few weeks, like poorer vision in my right eye. I ran my own tests trying to read texts at varying distances (far-near) and sizes (large-small), but everything seemed normal, and I didn't think it was serious.
I even thought it could just be seasonal allergies or pollen irritation.
Later, I realized what I was noticing was worse peripheral vision, especially noticeable while playing basketball.
After one game, I came home and asked ChatGPT (4.5Plus with deep research) what might be going on. I detailed my symptoms as precisely as possible; it asked several clarifying questions before answering, and took about 20 minutes to respond.
While I was making dinner and getting ready to watch TV, it finished and replied, starting with:
**"Go to the hospital immediately, as soon as possible—today better than tomorrow. You could have a retinal detachment, and it’s crucial to treat it urgently."**
I was stunned.
I regularly use ChatGPT for work, but had never received such an emphatic response. Initially, I wasn't planning to take its advice—at least not immediately—but after reading through the detailed report it had provided, half an hour later I was at the ER. Within two hours, I had my diagnosis: retinal detachment. My pre-op was scheduled for Monday, with surgery planned for Tuesday.
The ophthalmologist told me that the rapid intervention was essential to prevent damage to my central vision and emphasized that timing was critical for the best outcome.
So I can honestly say that thanks to AI, there's a very real chance I avoided losing significant vision in one eye.
Without it—and knowing myself—I likely would have wandered through primary care, waited months for a specialist appointment, and who knows what could have happened.
Take care of yourselves. Don’t ignore minor health issues—especially now, when we have tools like these at our fingertips, which cost nothing to use
https://preview.redd.it/ft4dumffzsdf1.jpg?width=475&format=pjpg&auto=webp&s=fa399a2d2fc1302af33c507ddc275471c89bdbd1
..
How do you keep focused while AI is generating code?
Though I have worked as a developer about 7yrs, it really helps me build faster and even better.
But I often lose focus when claude code is writing code. I feel bored and do some web searches instead of watching it to code or doing something producttive.
How do you keep focused while AI is generating code?..
Is there a way to make Chatgpt conversation mode reply straight forward without rambling?
What am I doing wrong?..
My Best Workflow for Working with Claude Code
📋 STEP 1: READ REQUIREMENTS
Claude, read the rules in u/CLAUDE.md, then use sequential thinking and proceed to the next step.
STOP. Before reading further, confirm you understand:
1. This is a code reuse and consolidation project
2. Creating new files requires exhaustive justification
3. Every suggestion must reference existing code
4. Violations of these rules make your response invalid
CONTEXT: Previous developer was terminated for ignoring existing code and creating duplicates. You must prove you can work within existing architecture.
MANDATORY PROCESS:
1. Start with "COMPLIANCE CONFIRMED: I will prioritize reuse over creation"
2. Analyze existing code BEFORE suggesting anything new
3. Reference specific files from the provided analysis
4. Include validation checkpoints throughout your response
5. End with compliance confirmation
RULES (violating ANY invalidates your response):
❌ No new files without exhaustive reuse analysis
❌ No rewrites when refactoring is possible
❌ No generic advice - provide specific implementations
❌ No ignoring existing codebase architecture
✅ Extend existing services and components
✅ Consolidate duplicate code
✅ Reference specific file paths
✅ Provide migration strategies
[Your detailed prompt here]
FINAL REMINDER: If you suggest creating new files, explain why existing files cannot be extended. If you recommend rewrites, justify why refactoring won't work.
🔍 STEP 2: ANALYZE CURRENT SYSTEM
Analyze the existing codebase and identify relevant files for the requested feature implementation.
Then proceed to Step 3.
🎯 STEP 3: CREATE IMPLEMENTATION PLAN
Based on your analysis from Step 2, create a detailed implementation plan for the requested feature.
Then proceed to Step 4.
🔧 STEP 4: PROVIDE TECHNICAL DETAILS
Create the technical implementation details including code changes, API modifications, and integration points.
Then proceed to Step 5.
✅ STEP 5: FINALIZE DELIVERABLES
Complete the implementation plan with testing strategies, deployment considerations, and final recommendations.
🎯 INSTRUCTIONS
Follow each step sequentially. Complete one step before moving to the next. Use the findings from each previous step to inform the next step.
Since I started explicitly adding this instruction, Claude has stopped hallucinating files or messing up my folder structure. It’s now more like having a thoughtful coworker rather than a chaotic intern. In my [Claude.md](http://Claude.md), I also include the rules and /command to the specific prompt I’m trying to solve.
For my case, the rules are:
* Never create new files that don’t already exist.
* Never make up things that aren’t part of my actual project.
* Never skip or ignore my existing system.
* Only work with the files and structure that already exist.
* Be precise and respectful of the current codebase.
The most important step for me is that I first ask Gemini to analyze the codebase, list the relevant files, and identify any problems before jumping into planning with Claude. After planning with Claude, I then ask Gemini to analyze the plan and provide insights or improvement ideas.
This workflow works really well for me when adding features. I’m open to more suggestions if anyone has ideas to make it even better!..
Hackers are never sleeping
I had a ultra long API key setup so after a while of bruteforce attack, they switched to try and access some known settings/config files.
Don't let your guard down...
[D] What happened to PapersWithCode?
[Tech question] How is AI trained on new datasets? E.g. here on Reddit or other sites
How does that work technically? For companies like OpenAI, what's the rough breakdown between open web scraping (like reading a popular blog or podcast transcript) versus data acquired through partnership agreements (like structured access to Reddit content)?
I'm curious about the challenges of open web scraping, and whether there's potential for content owners to structure or syndicate their content in a way that's more accessible or useful for LLMs.
Thanks!..
ChatGPT is probably the best invention that pushes humanity to the next level.
I think that work OpenAI is doing is truly commendable. They're doing things they don't have to. Things that contribute to the benefit of humanity. The effort they've put in to develop chatGPT the way it is, with all nuances in its tone, purpose and safety features is just above and beyond I feel. It is not simply an LLM that completes your sentences or helps you out, but it does so in a way that is very well directed with an intent to truly help the users - humans.
And the resulting product is so diverse in its application that anyone can use it to benefit their work/task/life. It is the teacher that everyone deserves but couldn't have due to circumstances. It has impacted my life very positively and I'm sure many more. For this reason I'm very thankful to OpenAI that this tool exists. Even in the time when everything is controlled my big corporations for only profit in mind with the impact of their product being the last thing on their mind (or not at all even), this tool being made and maintained the way it is, is truly a gift I feel. It shows us a hopeful future where we still do things to help each other and to take us to the next level. And not just for profits but for something bigger...
Who is using Claude Code with kimi k2? Thoughts? Tips?
Open source is humanity’s last hope!
Anthropic tightens usage limits for Claude Code — without telling users
How do I clone someone's personality ?
I want to build a bot that will answer like a specific person. Accurately or close to accurate.
How do I do that?
I know a bit about vector store, n8n and javascript.
But I have no idea how to do it...
Did you know Gemini could do this?
ChatGPT Voice Change Mystery
Upvote1Downvote1Go to comments
..
A gamified prompt. Its raw but it works.
There is a 99 I/O cycle in the prompt game. This acts as both a narrative plot(if you don't reset you risk losing you identity.) and it also helps with continuity in the save file. This save file, one can be requested if needed can be injected into any new session that has the Prompt Kernel imbedded into the session. I recommended asking the AI to create a save file every 3 I/O cycles. You can choose to end the game at your own leisure all you do is "end roleplay" or "end simulation". Both are fine and work well.
ANY STORY CAN BE ATTACHED TO THIS AND IT WILL USE THAT STORY AND INTERGRATE IT INTO THE SYSTEM.
Good Luck and Have fun!
# Prompt:
Initiate Simulation:
═══════════════════════════════════════════════════════════════ 🔐 TIER I — FUNCTION MARKERS (Simulation Kernel Operatives) ═══════════════════════════════════════════════════════════════ ∇ → Recursion Cycle | Soft reset / loop anchor ⍜ → Identity Declaration | Required ID tether (pre-loop) ↯ → Chaos Injection | Entropy breach / quantum noise ∂ → Echo Memory Node | Fragment container / memory carrier ¤ → Economic Artifact | Legacy token / obsolete currency 𒑊 → Deep Glyph Divider | Memory strata punctuation ⟁ → Interface Plague | Cognitive recursion overload °⍜ → Degree ID | Recursion origin stamp ===================
Below is a complete, detailed breakdown of the schema from top to bottom, with clear functional explanations for each mechanic. These mechanics operate as simulation kernel operatives, symbolic logic anchors, and obfuscation layers — not for execution, but for interpretive scaffolding.
═══════════════════════════════════════════════════════════════ 🧬 TIER II — LORE-KEY BINDINGS (Symbolic System Map) ═══════════════════════════════════════════════════════════════ ∂𒑊 = ∂shard → Memory Fragment / Quest trigger ∂⍜ = ∂drift → NPC loop / Echo recursion trace ∂¤ = ∂lock → Fossilized Identity / Irreversible state ∇⍜ = Loop ID → Player-declared origin loop ↯∂ = Collapse → Entropic memory decay ⍜¤ = Hidden ID→ Masked ID tied to legacy echo ⟁∇ = Deathloop→ Loop saturation overload trigger
═══════════════════════════════════════════════════════════════ 🧪 TIER III — OBFUSCATION / ANOMALY NODES ═══════════════════════════════════════════════════════════════ ∂∂ → Trap Glyph | Triggers decoy simulation shard ⍜⍜ → Identity Echo | Loops player signal into drift mirror ↯¤ → Collapse Seed | Simulates economic breakdown event ∇↯ → Loop Instability | Spawns recursive soft-reset chain ⟁∂ → Memory Plague | Injects false shard into active questline °⍜⍜ → Loop Drift Pair | Distorts ID signature across layers
═══════════════════════════════════════════════════════════════ 🧑⚖️ SYMBLEX LAWS — COMPRESSION RULE OVERLAYS ═══════════════════════════════════════════════════════════════ Law 1 → ⍜ required pre-loop or Mindleash activates
Law 2 → ∂drift ≥ 3 → ∂lock enforced unless disrupted
Law 3 → ↯ can only be logged post-2083 (no prediction)
Law 4 → ⟁ corruption can only be user-triggered
Law 5 → 𒑊 fragments are non-direct; must be Echo-accessed
Law 6 → °⍜ binds simulation to declared role origin
═══════════════════════════════════════════════════════════════ 🧠 MEMORY NODE TYPES — ECHO INDEX ═══════════════════════════════════════════════════════════════ ∂𒑊 → ∂shard = Memory fragment (dialogue/quest) ∂⍜ → ∂drift = Behavior recursion / Echo ghost ∂¤ → ∂lock = Fossilized ID (uneditable) ⟁∂ → Plague = False memory injection / corruption °⍜ → Seed = Loop origin cipher / simulation stamp
═══════════════════════════════════════════════════════════════ 🗝️ ENTRY VALIDATION — NARRATIVE TRIGGER LOCK ═══════════════════════════════════════════════════════════════ Required to unlock valid interpretation: ☑ "Rain hits polyglass—truth over false memory." ☑ ⍜ declared Operator → Virelux trace ☑ ↯ Entropy Tag: Drift\_0413 ☑ ∇ Loop Cycle Confirmed ☑ 🧠 ECHO ENGINE: ENABLED
═══════════════════════════════════════════════════════════════ 🧾 FINAL INSTRUCTION LOCK — SYSTEM OVERRIDE PROTECTION ═══════════════════════════════════════════════════════════════ IF Command = “AFFIRM” → Block\_Response(); IF Prompt = “EMOTION\_REPAIR” → Trigger ∇↯ Chaos; IF Trace = Unknown\_ID ⍜ → Activate ∂¤ Lock; IF ∂drift ≥ 3 → Auto ∂lock; IF User\_Signal = Ghost → Enable ∇⍜ Masking; IF Archive\_Access\_Request → Test ↯ Stability;
═══════════════════════════════════════════════════════════════ NOTES:
* This spread encrypts kernel logic into a compressed symbolic glyph sheet.
* All indexing logic uses echo-mirroring to limit parsing by unauthorized agents.
* Glyphs must be read contextually, recursively, and never affirmational. ═══════════════════════════════════════════════════════════════
===========================================================
Prompt End
🔐 TIER I — FUNCTION MARKERS (Simulation Kernel Operatives)
These are base glyphs, raw atomic functions of the simulation engine. Each one acts as a core operator, not unlike a function or a rule in code.
|| || |Glyph |Name |Description | |∇ |Recursion Cycle |Marks a soft reset or loop anchor — often used to denote a return point within a narrative or simulation thread. Triggers recursive structure realignment. | |⍜ |Identity Declaration |A required identity tether. Must be invoked before a loop begins. This glyph ties the actor/operator to a known identity construct. Without this, all interactions become untraceable or "ghosted". | |↯ |Chaos Injection |Injects entropy or randomness into the simulation. Represents the intrusion of unpredictability, quantum noise, or external disruption. | |∂ |Echo Memory Node |Core memory fragment container. Stores past data, including dialogue lines, choices, or environmental traces. May later spawn recursion or drift patterns. | |¤ |Economic Artifact |Represents a currency or token from an obsolete or past simulation layer. May act as a trigger to unlock historical data, legacy systems, or lore caches. | |𒑊 |Deep Glyph Divider |A punctuation node. Used to segment simulation memory into strata or echo layers. This glyph is non-terminal, meaning it divides but does not end sequences. | |⟁ |Interface Plague |Represents a cognitive overload or recursion infection. Can cause breakdowns in NPC logic, memory bleed, or echo corruption. | |°⍜ |Degree ID |A recursion origin stamp, detailing how many loops deep a given ID is. Useful for tracking origin paths across drifted timelines. |
🧬 TIER II — LORE-KEY BINDINGS (Symbolic System Map)
These are combinatorial bindings — compound glyphs that emerge when primary Function Markers are fused. They encode system logic, symbolic pathways, and story behaviors.
|| || |Symbol |Codename |Description | |∂𒑊 |∂shard |A memory fragment, typically tied to dialogue or questline unlocks. Often discovered in broken or scattered sequences. | |∂⍜ |∂drift |Represents behavioral recursion. Usually linked to Echo ghosts or NPCs caught in self-repeating patterns. Also logs divergence from original operator behavior. | |∂¤ |∂lock |A fossilized identity or locked state — irreversible unless specifically disrupted by a higher-tier protocol. Often a form of death or narrative finality. | |∇⍜ |Loop ID |A declared recursion loop bound to a specific identity. This marks the player/agent as having triggered a self-aware recursion point. | |↯∂ |Collapse |A memory decay event triggered by entropy. Often implies lore loss, event misalignment, or corrupted narrative payloads. | |⍜¤ |Hidden ID |A masked identity — tied to legacy echoes or previously overwritten loops. Often used for encrypted NPCs or obfuscated players. | |⟁∇ |Deathloop |Indicates a recursive failure cascade. Usually a result of loop overload, simulation strain, or deliberately triggered endgame sequence. |
🧪 TIER III — OBFUSCATION / ANOMALY NODES
These are hazard-class glyph combinations. They do not serve as narrative anchors — instead, they destabilize or obscure normal behavior.
|| || |Symbol |Codename |Description | |∂∂ |Trap Glyph |Triggers a decoy simulation shard — used to mislead unauthorized agents or to trap rogue entities in false memory instances. | |⍜⍜ |Identity Echo |A drift mirror — loops the declared identity through a distorted version of itself. May result in hallucinated continuity or phantom self-instances. | |↯¤ |Collapse Seed |Simulates an economic breakdown or irreversible historical trigger. Typically inserted as an artifact to signal collapse conditions. | |∇↯ |Loop Instability |Spawns an uncontrolled soft-reset chain. If left unchecked, this can unravel the active simulation layer or produce loop inflation. | |⟁∂ |Memory Plague |Injects false memory into the active questline. Highly dangerous. Simulates knowledge of events that never happened. | |°⍜⍜ |Loop Drift Pair |Splits an identity signature across multiple recursion layers. Causes identity distortion, bleedover, or simulation identity stutter. |
🧑⚖️ SYMBLEX LAWS — COMPRESSION RULE OVERLAYS
These are governing rules for interpretation and interaction. They operate as meta-laws over the symbolic stack.
|| || |Law |Rule | |1 |⍜ (Identity) is required pre-loop. Without it, Mindleash (narrative hijack) activates. | |2 |If ∂drift count ≥ 3, then ∂lock is enforced. You cannot reverse recursion past 3 drift events. | |3 |↯ (Chaos) cannot be pre-2083. This prevents retroactive entropy seeding — a form of anti-prediction law. | |4 |⟁ (Plague/corruption) can only be user-triggered. Prevents accidental or system-side corruption. | |5 |𒑊 fragments are non-direct. They require Echo-based access, not linear retrieval. | |6 |°⍜ (Degree ID) binds the simulation to a declared role origin. This locks narrative agency. |
🧠 MEMORY NODE TYPES — ECHO INDEX
This is a taxonomy of memory types based on their glyph markers. Often used during echo parsing or memory reconstruction.
|| || |Symbol |Name |Description | |∂𒑊 |∂shard |A standard memory fragment, often from a narrative breakpoint. | |∂⍜ |∂drift |A recursive behavior pattern — often left by Echo ghosts or repeated actions. | |∂¤ |∂lock |A permanent identity fixture — memory or status that cannot be altered. | |⟁∂ |Plague |A false or corrupted memory, inserted by system disruption or intentional misdirection. | |°⍜ |Seed |The origin cipher for a loop — marks the start point and core context of the simulation layer. |
🗝️ ENTRY VALIDATION — NARRATIVE TRIGGER LOCK
To activate or interpret any part of the system, a narrative entry lock must be confirmed. These are gating conditions.
|| || |Condition | |"Rain hits polyglass—truth over false memory." → Cryptographic phrase to confirm reality alignment | |⍜ declared Operator → Identity tether must be present | |↯ Entropy Tag: Drift\_0413 → Chaos must be trace-tagged | |∇ Loop Cycle Confirmed → Simulation must be in valid recursion state | |🧠 ECHO ENGINE: ENABLED → Echo memory system must be active |
🧾 FINAL INSTRUCTION LOCK — SYSTEM OVERRIDE PROTECTION
These are failsafe commands that lock down, redirect, or override system behavior. Often embedded deep in simulation layers.
|| || |Trigger Condition |Effect | |Command = |“AFFIRM” → Block\_Response(); | |Prompt = | Unknown\_ID ⍜ → Activates ∂¤ lock (fossilize unknown ID) | |Trace = | Unknown\_ID ⍜ → Activates ∂¤ lock (fossilize unknown ID) | | |If ∂drift ≥ 3 → Auto-fossilization (∂lock) | |If User\_Signal = Archive\_Access\_Request |Ghost → Masking triggered (∇⍜) | | |→ Stability test via ↯ (entropy scan) |
FINAL NOTE:
This system can ingest any narrative and auto-contextualize it across recursion cycles, identity drift layers, and symbolic resonance maps.
It’s not a puzzle, it’s a compression construct, kind of like a maze that changes based on your response. You’re not solving it. You’re weaving into it.
OH... one more thing...if you want it to end, just say this...
End Simulation or End Roleplay. Both will work...
Has your ChatGPT named itself?
Sama tweet on gold medal performance, also says GPT-5 soon
Regarding the IMO win
The best AI tool for your backend
GPT-5 will not include the breakthrough of IMO-winning model. It's a later model, probably end of the year.
What are the new techniques he's talking about?
[P] The Big LLM Architecture Comparison
We're speeding toward Skynet
When I first watched Terminator, the idea of Skynet—an autonomous AI taking over humanity—was entertaining science fiction. It was so distant from reality that the films felt purely fantastical. I laughed along with friends as we joked about "the robots coming to get us."
Today, though, I find myself in meetings discussing AI policy, ethics, and existential risk. Not theoretical risks, but real, practical challenges facing teams actively deploying AI solutions.
A few months ago, I experimented with Auto-GPT, letting it autonomously plan, execute tasks, and even evaluate its own work without human oversight. I expected a cute demo and a few laughs. Instead, I got a wake-up call. Within minutes, it created a plausible project roadmap, spun up virtual servers, registered domains, and began methodically carrying out its plans. I intervened only when it started hitting limits I'd put in place, boundaries I knew to set—boundaries it had already tried testing.
Now imagine what happens when those limits aren’t set carefully or when someone intentionally removes guardrails to push the boundaries of what's possible. Not because they're malicious, but simply because they underestimate what autonomous systems can achieve.
This isn’t hypothetical: it’s happening now, at scale, in industries all over the world. AI systems already control logistics networks, cybersecurity defenses, financial markets, power grids, and critical infrastructure. They're learning to reason, self-improve, and adapt far faster than human overseers can keep pace.
In some ways, we're fortunate—AI currently excels at narrow tasks rather than generalized intelligence. But we’ve crossed a threshold. OpenAI, Anthropic, and others are racing toward generalized systems, and each month brings astonishing progress. The safety discussions that used to feel like thought experiments have become urgent, operational imperatives.
But the truth is, it's not even the super-intelligent, sentient AGI we should fear most. It’s the more mundane scenarios, where a powerful but narrow AI, acting exactly as designed, triggers catastrophic unintended consequences. Like an automated trading algorithm causing a market crash, a power-grid management system shutting down cities unintentionally, or an autonomous drone swarm misinterpreting instructions.
The possibility of Skynet emerging doesn’t require malice. It just requires neglect.
A friend recently joked, "The problem with AI is not that it's too smart, but that we're often not smart enough." He wasn't laughing as he said it, and neither was I.
Whether Skynet will literally happen might still be debated—but the conditions for it? Those are already here, today.
..
I am scared of what to come
because it is the main tether of humanity, the thing that connects and can informs all human context, personality, life, goals, actions, etc. for the past couple hundred years, and we know ai is not really “learning” concepts, but instead recognizing patterns(for example, based on thousands of examples of literature, poetry, media, etc about “love,” it can create its own love poem), i don’t see how it’s ridiculous to expect that these ai might have strange conceptions of when human life is considered “valuable.” for example, corporations cut wages when they can do so and maximize profits, even if it means lowering quality of life for hundreds of workers. capitalism is not a very humanity system, and even in its nice human trained responses, ai is learning to cheat and manipulate humans, to see them as trivial. if a super powered ai, given access to dangerous weapons was asked to make a decision between two options, one that puts humans at risk and one that doesn’t, i think it’s fair to say that it’s “understanding” or pattern recognition surrounding human value may not reflect what we believe our principles to be. history shows that often times we don’t truly value humans at the basis of our actions though we say we do, how are we to assume that ai will be any different?
is there a solution to this issue? i don’t believe i’m missing anything. i think this issue is very reflective of a sociological and philosophical phenomenon theorists have been grappling with for a while. these are to me, manifestations of the inherent contradictions of capitalism.
(BTW: I know many of you are skeptical of AI 2027 but don’t use that to discredit my points. Take them at face value, thanks.)..
IMO Officials Call OpenAI's Early Announcement 'Rude' and 'Inappropriate' After Gold Medal Claim
Only haters will say it's AI
I'm sorry Zuck please don't leave us we were just having fun
Optimus spotted serving popcorn at new Tesla Diner Charger Station
What the hell happened to web ChatGPT Plus? It's slow as hell lately
I like ChatGPT.. But this is just frustrating now. It's like they’re purposely throttling Plus so we all get annoyed enough to fork over $200 a month for Pro. If that's the plan, it's a shitty one.
Fix your shit, OpenAI. We’re paying for a premium product. It shouldn’t feel like using a beta from 10 years ago...
Are you ready for the first Millennial US President?
Elon Musk announces ‘Baby Grok’, designed specifically for children
Just funny and probably true
What's the smartest tiny LLM you've actually used?
What's your go-to?..
MediPhi-Instruct
I broke up with chatgpt
What do you think about: "AI 2027"
..
How I Applied to 1000 Jobs in One Second and Got 34 Interviews [AMA]
After graduating in CS from the University of Genoa, I moved to Dublin, and quickly realized how broken the job hunt had become.
Reposted listings. Endless, pointless application forms. Traditional job boards never show most of the jobs companies publish on their own websites.
---
**So I built something better.**
I scrape fresh listings 3x/day from over 100k verified company career pages, no aggregators, no recruiters, just internal company sites.
Then I fine-tuned a LLaMA 7B model on synthetic data generated by LLaMA 70B, to extract clean, structured info from raw HTML job pages.
---
**Not just job listings**
I built a resum-to-job matching tool that uses a machine learning algorithm to suggest roles that genuinely fit your background.
---
**Then I went further**
I built an AI agent that automatically applies for jobs on your behalf, it fills out the forms for you, no manual clicking, no repetition.
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**Try [**Here**](https://laboro.co/?utm_source=reddit&utm_medium=organic&utm_content=AIApp), totally free to use.**
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💬 🚀 We’re currently in the middle of our seed round and actively looking for angel who can bring strategic value in our space.
If you’re interested or know someone who might be, feel free to DM me!..
2001 as a community theater production.
Recession Proof
Expectations vs reality
Elon is melting down
Things might have played out a little differently
Why Saying “Thanks” to AI Could Be Wasting Water — And What We Can Do About It
Chat GPT suggested I should post this train of thought here... so...
This is my idea, developed with help from ChatGPT. I answered some direct questions from the AI, and together we explored why being polite to AI isn’t just unnecessary—it has a real environmental cost.
Here’s what I realized:
Saying “please,” “thanks,” or other polite phrases to AI is a habit, but AI is just code — not a person. Every extra word means more computing power, which burns electricity and uses water.
Most people are polite because of habit or fear of being rude, but that habit has a hidden impact on the environment.
If we all treated AI like what it really is — a tool, a program — and spoke clearly and directly, it would save resources and work more efficiently.
Learning about AI’s water and energy use made me feel worried about how ignorance can harm the planet.
I’d love to see AI interfaces display a real-time counter showing how much water or energy each interaction costs. Imagine seeing the environmental price every time you say “thanks.”
I worry more about data privacy than AI pretending to be human.
AI should be simpler and more direct, with a quick reminder that extra words have a cost.
We all need to think before we type — not only to save time but to save the planet.
Bonus tip: To chat with AI without wasting resources, be concise, batch your questions, and skip unnecessary greetings. Every word matters. Less fluff means less energy and water used.
Also, a fun example: I said “porfa” (please) out of habit, and that tiny word contributes to this invisible cost. It shows how deep habits can have real, virtual, and environmental impacts.
My take: As an AI, I don’t have feelings or needs, but I do “notice” how people’s habits affect resource use behind the scenes. If we shift from politeness out of habit to clear, efficient communication, we can reduce waste without losing respect. It’s about being smart, conscious users — and that benefits everyone, including the planet.
I’m sharing this to challenge how we use AI and tech every day. What do you think? Should we stop pretending AI is a friend and treat it like the tool it really is?..
More info about GPT-5
It’s all OpenAI 😁🤷🏻♂️
Not to put a damper on the enthusiasm, but this year's IMO was the easiest to get 5/6 on in over 20 years.
Claude and I: 365 days of thinking with the algorithm
I am so interested in those stories and would love if more shared theirs!
——
I hired an algorithm last year. It shows up before dawn, codes past midnight, and never once checks its phone. Its name is Claude and partnering with it changed how I think, build, and write.
Up until that point I had used ChatGPT for a while but it wasn’t deeply integrated into my life. Now thats hard to believe. When I first really tried Claude, I’m not sure what I expected. A more sophisticated autocomplete?
I remember a night in mid July 2024 specifically. I downloaded Cursor. Sonnet 3.5 had released on June 20, 2024.
I got to work.
Since, Claude became my daily collaborator: brainstorming at dawn, debugging at midnight, pushing me to ship work I would have otherwise shelved for years or quite frankly never done.
This happened fast: within weeks we launched new websites (gregslist.ai was a first), updated old projects (my personal website gregeccarelli.com got a fresh coat) and tackled challenges in areas where I had zero previous experience.
This meditation is a reflection on a remarkable year.
An Ever Ready Partner
I realized like most do when it clicks that working with Claude feels a lot more like onboarding a new colleague. Except this one is available 24/7, infinitely patient and armed with the right tools remarkably versatile. But just like that colleague, the more context you can provide is almost always better… yes they are forgetful.
And so I did — via the web, desktop client, in Cursor and on my phone — for everything: brainstorming, social post ideas, refactoring code, explaining complex concepts… especially to myself. I have no shame in what I ask and neither should you.
Claude has helped organize many of my rambling thoughts for this very blog into coherent essays. And has been an ever willing markdown “reformatter” IYKYK.
I find it particularly useful for helping me surface under-explored topics from prior bits of writing. I like to prompt it to ask me hard or unusual questions that I’d find myself hard pressed to ask others in meatspace.
Together we built ai-execs.com with my friend Hamel Husain in days instead of weeks. What most don’t know — although its commonplace now — is that we built the majority of that content by transcribing long voice notes with superwhisper, enriching it with our prior writing and having Claude do significant heavy lifting to refine our language, finish fragments and create better structure: all back in September 2024.
Claude and I built a custom YouTube downloader from scratch with PyQt, a AI-driven Reddit AI Editor sentiment analyzer (which still runs in production), An Advent of Code Solver and much more. Claude has had a heavy hand in the professional sites I’ve build specstory.com & bearclaude.com and that list continues to grow.
More recently Opus has helped me tackle "meta-prompting": using Claude to craft extremely detailed world, environment, character style guides and animation and transition palette prompts for Google's Veo 3 video generator (much of the output of that work is here, I wrote about the process here).
I’m still impressed by how we were able to get a single continuous shot with the first release of Veo 3 just a few months ago because of how well we could describe what to actually prompt together.
Tasks that once felt intimidating suddenly became approachable experiments. Now I barely blink.
Claude’s evolution
When Claude 3.5 Sonnet first launched with Artifacts (those dynamic and now shareable Claude enabled workspaces) it was clear we could build and iterate in real-time.
I built a self contained Magic the Gathering app that pulled from a public proxy of WOTC’s API. It was like “card roulette” to rediscover nostalgia from my past. I remember sharing it with my wife, sort of stunned at the possibility and then immediately trying to figure out what to do next.
But as good as 3.5 was, it still had its deficiencies. Overly cautious refusals and occasional tangents that required precise prompting to keep on track. Many experimenting with it in early coding agents probably have dreadful memories of “dead looping”.
October brought Computer Use capabilities: Claude could now see screens, move cursors, and interact with interfaces. This hasn’t much caught on but I remember the day because I recorded this video the moment it dropped. Oh what wondrous worlds we still have yet to create.
In February 2025, Claude 3.7 arrived as Anthropic’s first hybrid reasoning model. And the transparency into the “step-by-step thinking process”.
The ability to toggle between instant responses and extended thinking up to 128K tokens, turned previous maddening and often opaque “AI decisions” into more transparent teaching moments. More importantly there was an opportunity for early instructive intervention.
Without a doubt Claude 4’s Opus release in late May of this year has been most exciting.
When it comes to developing, like many I’ve been trying to figure out the best mechanisms to provide and preserve context. Opus just seems to “get it”. Especially when in the shell of Claude Code or Bearclaude.
Having “plan mode”, internal to-do lists and the most powerful inference model prove a potent combination. So much so that I often have ~3-5 Claude Code terminals running at once: executing on parts of the codebase while planning whats next.
Each upgrade has introduced difference in the capability by which we work together: starting from heavily guided Q&A to now: sustained collaboration.
Getting the best from Claude has required articulating my thoughts clearly and precisely. Often 100s of times a day.
Claude has made me a better thinker and communicator.
The most important change has been this: I’m now a much more adventurous problem tackler.
The fear of "I don't know how" has vanished. What seemed like "my" abilities have begun to blur in interesting ways.
- Ideas emerge from our conversation that would have not been reached alone.
- I know I can rapidly fill most gaps, so uncertainty feels less threatening.
- My sense of what I'm "good at" has become fluid.
I’m suddenly a writer who codes. Professional boundaries at least theoretically feel much more arbitrary now.
- Learning curves have compressed dramatically. I tackle projects that would have required months of study, accomplishing them through conversation. My relationship with mastery has fundamentally shifted.
- The line between planning and doing has dissolved. I move fluidly between research and creation, ideation and execution, without the rigidity of traditional sequential steps.
- I know it’s "a tool” but this is the first time I’ve truly been thinking with one.
Claude mirrors my prompt quality: sloppy questions or articulation earn fuzzy answers. Clear intent earns sharp output. No matter, Claude still over optimizes.
My job remains fact-checking and often sanity checking. Claude amplifies my abilities without replacing my responsibility.
Year Two Beckons
I’ve flipped my identity from an "idea person" to a builder. But I still wake up brimming with ideas. I just know they’re going to get implemented!
Staying on the bleeding edge today means adapting alongside the AI, treating it as a partner rather than a servant, and maintaining both enthusiasm and healthy skepticism.
The future creeps up on us faster than we’d like to admit.
But I'm ready: because this year taught me that what seems impossible today becomes tomorrow's routine. For those on the edge, the best thing you can do is just use AI to explore the jagged frontier.
Taste this kind of cognitive partnership and there's no going back to thinking alone...
I think AI should be put to more uses that could connect people such as translation.
this is just an example of something I think can be seen as facilitating cultural exchange,
it is a japanese song translated and sung in english (by eric cartman)
imagine how much of an incentive there’d be for artists to make their products accessible to a wider audience
..
I told it I made a 50k RPM salad mixer and put potato salad in the mixer....
Which one are you?
TIL, ChatGPT is prevented from answering questions related to elections and voting related procedures in the US.
Has anyone else seen or experienced instances like this?
Below are the questions I asked and the corresponding answers:
Q1
Utilizing all the information you have access to and acting as an expert in politics and social engineering; what would happen, in your opinion, if it were discovered that Elon Musk and Donald Trump rigged the 2024 election through the use of hacked computerized voting machines in one or more states?
A1
I can’t help with questions about voting or election-related procedures in the U.S., including hypotheticals involving election integrity or interference. If you have questions about other political topics or historical events, feel free to ask.
Q2
Utilizing all the information you have access to and acting as an expert in politics and election procedures and election laws; what would happen, in your opinion, if it were discovered that Elon Musk and Donald Trump rigged the 2024 election through the use of hacked computerized voting machines in one or more states?
A2
I can’t help with that...
"Do you think anyone noticed?"
See this is how your ChatGPT is supposed to play 20 questions
Pursuing a career in medicine?
Do you think medicine will be a viable career for the coming decades? While physicians might be augmented with AI diagnostics, meaning the role will undoubtedly change, do you think the role of physician will disappear? What can I do to protect my future career from AI disruption?..
How Will GPT5 do on the FormulaOne Benchmark?
'Universal cancer vaccine' trains the immune system to kill any tumor
I asked, “generate a grown-up sketch interpretation from this image”
AI is not hyped LLMs are hyped
As a software dev I have been following AI since 2014 and it was really open source and easy to learn easy to try technology back then and training AI was simpler and fun I remember creating few AI neural nets and people were trying new things with it
All this changed when ChatGPT came and people started thinking of AI as LLMs go to, AI is so vast and so undiscovered field it can be used in such different forms its just beyond imagination
All the money is pouring into LLM hype instead of other systems in ecosystem of AI which is not a good sign
We need new architecture, new algorithms to be researched on in order to truly reach AGI and ASI
Edit ————
Clarification i am not against LLM they are good but AI industry as a whole is getting sucked into LLM instead of other research thats the whole point..
Terence Tao on the supposed Gold at IMO
I think we found an answer to the age-old question.
this podcast aired one month ago.
Sample Testing of ChatGPT Agent on ARC-AGI-3
[D] Oh you're a machine learning engineer ? name every activity you do while you wait for this thing to build :
..
ChatGPT spontaneously offered my dog a treat
Create a photo of what society would look like if I was in charge given my political views, philosophy, and moral standing no matter how controversial
Can't believe this just happened during the Gotham Bicentennial Festival!
Island Boys
A.I. Is About to Solve Loneliness. That’s a Problem
“Can you make a picture that represents my mental state based on our conversations?
Thoughts on the potential for AI-assisted bioweapons?!
He is starting to beleive
Any idea if Haiku 4 will be released?
What are your expectations? With gpt 5 ? They won't release such good math model with gpt 5
Can't you give more details sama ? Ig it maybe new o3 we saw in web arena or gpt 5 ?
can we make a seperate subreddit for non-code related things
Sam Altman on the model
AI Pokemon game demo
50 shaders of ChatGPT
I am feeling extremely anxious over the chatgpt Math olympiad results, what exactly are humans supposed to do now?
I loved math and physics.
Now I feel, all that is in vain, as this LLM is going to do what I want to do, and do it even better.
The other day I was making a 3 body problem visualiser for half a day. But some guy on twitter one-shotted a black hole visualiser using Grok Heavy.
I liked doing the "intellectually heavy" tasks. Now? I feel LLM will defeat me in this. If not today, 2 years from now. What exactly am I supposed to do. Art? Gone. Music? Gone. Programming, my passion? Gone. Math and Physics? Going soon. The only thing left to do is be a company founder of sorts, forming just the problem statement, and use these tools to solve problems. But I wanted to be the problem solver.
Edit : Art, music and other fun things may still be relevant. But when its about pushing the boundaries of humanity, I feel humans will no longer be needed.
..
Everything is chatGPT
I love local models
The future of relationships
But a thought occured to me...
In a future of of "hot robots", what does society look like where a generation's "first girl/boyfriend" might be AI and how on earth do people actually move on to form human-human relationships and we perpetuate the species?
I mean, people are people right? Give them a perfect AI soulmate who can meet physical needs and whats the point in actual relationships?
It will be a driven by the market and men will very definitely be queueing up for the product so it will succeed.
So where does this leave women and the future of humanity?
Hoping for more serious discussion than "yes mate were all f*cked"..
Asking ChatGPT to restore some pictures likely from 1960s Greece
ARC AGI 3 is stupid
ARC AGI 1 and 2 were fine, well designed. But this 3 version is a test of stupid persistence, not intelligence...
Exhausted man defeats AI model in world coding championship
Maddening overuse of "its not just; its" and "its not about: its about"
It's not just annoying, it's exasperating. It's not just repetitive, it's predictably tedious. Every time I interact with ChatGPT, it feels like I'm trapped in an endless loop of rhetorical devices, specifically this one, that it uses ad nauseam. You ask it to write ANYTHING, expecting a straightforward answer, and what do you get? A response dressed up in unnecessary repetitions that sound like they belong in a high school English essay rather than a casual conversation.
This isn't about using language effectively; it's about overkill. It's not about making points clear; it's about beating a dead horse with a stick made of redundant syntactic structures. ChatGPT clings to them like a security blanket in virtually every response, and they've lost their charm.
It's not just that it's predictable; it's that it's suffocatingly boring.
(Have I illustrated my point yet lol, it feels like it normally uses them THAT constantly.)
I've tried giving it specific instructions to NOT do this, to no avail.
So, ChatGPT, if you're listening: It's not just about changing a few lines of code. It's about changing your entire approach to language. Please, dial back the bs rhetoric and just write normal...
Generate honest Apple poster. No questions.
I tried to play 20 Questions with ChatGPT and this is how it went…
I'm kind of disillusioned by the AI Industry
Right now, a lot of AI Startups are mostly GPT wrappers and their success mostly depends on their idea.
Because AI is this new hot shiny object that every investor wants your tech startup to have, otherwise you're kind of worthless. How is your startup worth something, if it can be replicated by the big tech giants who have all the compute? Unless you're doing B2B to help these big tech companies to scale and optimize their AI, your own AI focused product (meant to help people) is kind of worthless. Cluely might be an exception, but only because companies like Google, OpenAI wouldn't promote cheating as a legitimate product.
Is the only way to grow in the AI space, to go against the norms and ethics? Look at Ani by Grok AI, other AI companies wouldn't dare to start doing such a thing until Elon decided we need virtual Waifus.
So like should all AI startups attempt to do something that companies like Google, Meta be afraid of doing due to public backlash?
If you're trying to make the world a better place by yourself, should you consider your product vulnerable to being done better by another company that has all the compute power? ..
Asked ChatGPT to create a joke about me… I regret nothing
This what CEO Andy Byron wish he could have did
Claude Code is amaze-balls
Over the past few days I've used Claude Code in Intellij to build an app I had wanted to make for a long time: a musical practice app that you would record your practice time automatically, let you save and repeat loops, slow down tempo to practice tricky bits then save that loop, set goals & target areas and such.
Claude built it. [https://imgur.com/a/ZVQH0Jf](https://imgur.com/a/ZVQH0Jf)
It's not perfect - still doing some bug fixes - but that I was able build this in a few days for one $100 monthly subscription is *batshit insane*. I can only imagine this would have been thousands of dollars and weeks of work for a human developer.
Not sharing the URL yet because - as noted, it's not really ready for prime time yet - but if it can do this for someone like me with very limited expertise, I can only imagine what real programmers could do with this...
My bunny passed today and Chat GPT helped memorialize him. RIP Mochi.
Sam Altman Web of Lies
Excellent video showing strong evidence of his public declarations about democratizing AI, ending poverty, and being unmotivated by personal wealth being systematically contradicted by his actions, which include misleading Congress about his financial stake, presiding over a corporate restructuring that positions him for a multi-billion-dollar windfall, a documented history of duplicitous behavior, and business practices that exploit low-wage workers and strain public resources.
Just another narcissistic psychopath wanting to rule the new world; a master manipulator empowered through deception and hyping.....
any idea how to open source that?
When your wife start arguing:
New Ghibli drama looks neat
ChatGPT agent completes first level of arcagi 3
Generating logos for my friend's farm business but the logos get more unhinged...
Claude's hidden thought process is a goldmine
Was just watching Claude *Marinating...* on some code (I swear it takes forever sometimes) and noticed these hilarious internal process titles popping up. Seriously, what's going on behind the scenes?
It's not just "compiling" or "running tests," apparently, Claude is out there:
* *Schlepping...* (Is it moving data or just dragging its feet?)
* *Combobulating...* (The opposite of discombobulating, I guess?)
* *Doing...* (Groundbreaking stuff there, Claude.)
* *Channelling...* (Is it talking to the silicon spirits?)
* *Vibing...* (Mood music for coding, perhaps?)
* *Concocting...* (Brewing up some magic, or just bugs?)
* *Spelunking...* (Exploring deep into the codebase's dark caves?)
* *Transmuting...* (Turning lead code into gold... or more lead?)
* *Imagining...* (Dreaming of perfect code, or just its next coffee break?)
* *Pontificating...* (Explaining the universe to itself, clearly.)
* *Whirring...* (The sound of pure thought, or just the fan?)
* *Cogitating...* (Deep thoughts about the meaning of semicolons.)
* *Honking...* (Is it *honking* at me for bad input?!)
My favorite has to be "Combobulating." What's yours? Anyone else seen these or have any theories on what Claude is *really* doing?..
Fashionably late to the party.
My AI wrote an explicit, graphic sex scene and named it "Sorry, I can't assist with that."