aifeed.fyi - AI news today

17 February 2026

Ai Top News

techcrunch.com

Blackstone backs Neysa in up to $1.2B financing as India pushes to build domestic AI infrastructure

Neysa, an Indian AI infrastructure startup, has secured backing from U.S. private equity firm Blackstone as it scales domestic compute capacity amid India’s push to build homegrown AI capabilities.

Blackstone and co-investors, including Teachers’ Venture Growth, TVS Capital, 360 ONE Assets, and Nexus Venture Partners, have agreed to invest up to $600 million of primary equity in Neysa, giving Blackstone a majority stake, Blackstone and Neysa told TechCrunch. The Mumbai-headquartered startup also plans to raise an additional $600 million in debt financing as it expands GPU capacity, a sharp increase from the $50 million it had raised previously.

The deal comes as demand for AI computing surges globally, creating supply constraints for specialized chips and data center capacity needed to train and run large models. Newer AI-focused infrastructure providers — often referred to as “neo-clouds” — have emerged to bridge that gap by offering dedicated GPU capacity and faster deployment than traditional hyperscalers, particularly for enterprises and AI labs with specific regulatory, latency, or customisation requirements.

Neysa operates in this emerging segment, positioning itself as a provider of customized, GPU-first infrastructure for enterprises, government agencies, and AI developers in India, where demand for local compute is still at an early but rapidly expanding stage.

“A lot of customers want hand-holding, and a lot of them want round-the-clock support with a 15-minute response and a couple of our resolutions. And so those are the kinds of things that we provide that some of the hyperscalers don’t,” said Neysa co-founder and CEO Sharad Sanghi.

Ganesh Mani, a senior managing director at Blackstone Private Equity, said his firm estimates that India currently has fewer than 60,000 GPUs deployed — and it expects the figure to scale up nearly 30 times to more than two million in the coming years.

That expansion is being driven by a combination of government demand, enterprises in regulated sectors such as financial services and healthcare that need to keep data local, and AI developers building models within India, Mani told TechCrunch. Global AI labs, many of which count India among their largest user bases, are also increasingly looking to deploy computing capacity closer to users to reduce latency and meet data requirements.

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On June 23 in Boston, more than 1,100 founders come together at TechCrunch Founder Summit 2026 for a full day focused on growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with tactics you can apply immediatelySave up to $300 on your pass or save up to 30% with group tickets for teams of four or more.
TechCrunch Founder Summit: Tickets Live
On June 23 in Boston, more than 1,100 founders come together at TechCrunch Founder Summit 2026 for a full day focused on growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with tactics you can apply immediatelySave up to $300 on your pass or save up to 30% with group tickets for teams of four or more.
The investment also builds on Blackstone’s broader push into data center and AI infrastructure globally. The firm has previously backed large-scale data centre platforms such as QTS and AirTrunk, as well as specialized AI infrastructure providers including CoreWeave in the U.S. and Firmus in Australia.

Neysa develops and operates GPU-based AI infrastructure that enables enterprises, researchers, and public sector clients to train, fine-tune, and deploy AI models locally. The startup currently has about 1,200 GPUs live and plans to sharply scale that capacity, targeting deployments of more than 20,000 GPUs over time as customer demand accelerates.

“We are seeing a demand that we are going to more than triple our capacity next year,” Sanghi said. “Some of the conversations we are having are at a fairly advanced stage; if they go through, then we could see it sooner rather than later. We could see in the next nine months.”

Sanghi told TechCrunch that the bulk of the new capital will be used to deploy large-scale GPU clusters, including compute, networking and storage, while a smaller portion will go toward research and development and building out Neysa’s software platforms for orchestration, observability, and security.

Neysa aims to more than triple its revenue next year as demand for AI workloads accelerates, with ambitions to expand beyond India over time, Sanghi said. Founded in 2023, the startup employs 110 people across offices in Mumbai, Bengaluru, and Chennai...

Yesterday

Open Source

/r/LocalLLaMA

Difference Between QWEN 3 Max-Thinking and QWEN 3.5 on a Spatial Reasoning Benchmark (MineBench)

Honestly it's quite an insane improvement, QWEN 3.5 even had some builds that were closer to (if not better than) Opus 4.6/GPT-5.2/Gemini 3 Pro.

Benchmark: [https://minebench.ai/](https://minebench.ai/)
Git Repository: [https://github.com/Ammaar-Alam/minebench](https://github.com/Ammaar-Alam/minebench)

[Previous post comparing Opus 4.5 and 4.6, also answered some questions about the benchmark](https://www.reddit.com/r/ClaudeAI/comments/1qx3war/difference_between_opus_46_and_opus_45_on_my_3d/)

[Previous post comparing Opus 4.6 and GPT-5.2 Pro](https://www.reddit.com/r/OpenAI/comments/1r3v8sd/difference_between_opus_46_and_gpt52_pro_on_a/)

*(Disclaimer: This is a benchmark I made, so technically self-promotion, but I thought it was a cool comparison :)*

--- TOP COMMENTS ---
I can feel this. My initial impression for Qwen 3.5 (incl. VL) is it's extremely impressive for a hybrid linear-linear-linear-full attention model, and except a few hiccups, it is almost competitive with some of the frontier models in terms of robustness. Maybe not as good for agentic use (which I did not test) as its output does not smell of forced mini-CoT post-training common for "agentic-maxxed" models.

Hiccups I see:

1. Like Qwen Next and many other hybrid linear models, the first token after thinking is a bit of hit and miss with longer prefill (70k\~). There are a few misses where it completely ignores the instruction (totally random) though it usually is okay.
2. As usual for Qwen, it drifts between language

BTW this Plus and opensource thing is confusing. I tested those models in direct Alibaba Cloud account and there is no clear explanation of differences between them. I assume Plus is opensource + ctx extended to 1m + some tool calling enabled by default. It has search function in Alibaba Cloud btw.

---

This is the kind of self promotion the sub needs. It's a good benchmark...

12h ago

Companies

/r/ArtificialInteligence

OpenAI hired the OpenClaw creator. The military used Claude in the Venezuela raid. The Pentagon may drop Anthropic's $200M contract. Disney accused ByteDance of an IP 'smash-and-grab.' (15 Feb 2026 recap)

Here are the most important news from the past two days:

**OpenAI hires OpenClaw creator Peter Steinberger to build 'next-generation' AI agents**

OpenAI hired Peter Steinberger, creator of the viral AI agent OpenClaw, to lead development of next-generation personal agents. Sam Altman called him "a genius" and said multi-agent capabilities will "quickly become core to our product offerings."

OpenClaw will move to an independent foundation while remaining open source with OpenAI support. Steinberger chose OpenAI over starting a company, saying "what I want is to change the world, not build a large company." His goal: an agent "even my mum can use."

The hire comes with baggage: security researchers found thousands of exposed OpenClaw instances vulnerable to remote code execution and dozens of malicious skills on its marketplace containing keyloggers and credential stealers. ([read the full story](https://7min.ai/news/openai-hires-openclaw-creator-peter-steinberger/))

**US military used Anthropic's Claude in the operation to capture Venezuela's Maduro**

The Pentagon deployed Claude during the January 3rd raid on Nicolás Maduro's fortified palace in Caracas, through Anthropic's partnership with Palantir. Delta Force commandos used the AI during the active operation—not just in planning. People were shot during the breach.

An Anthropic executive reached out to Palantir afterward to ask whether Claude had been used, "in a way to imply that they might disapprove of their software being used, because obviously there was kinetic fire during that raid." Claude was the first AI model the Pentagon brought into its classified networks. The revelation has intensified a growing rift between the "safety-first" AI lab and its biggest government client. ([source](https://www.axios.com/2026/02/13/anthropic-claude-maduro-raid-pentagon))

**Pentagon threatens to drop Anthropic's $200M contract over military AI limits**

The Pentagon is considering severing its relationship with Anthropic because the company won't remove all restrictions on military use of Claude. The Defense Department is pushing four AI labs—OpenAI, Google, xAI, and Anthropic—to allow "all lawful purposes," including weapons development and intelligence collection. OpenAI, Google, and xAI agreed to lift their guardrails. Anthropic refused.

Anthropic insists two areas remain off limits: mass surveillance of Americans and fully autonomous weapons. The contract, signed last summer, is valued up to $200M. Internally, Anthropic engineers are uneasy about Pentagon work. The standoff puts the company's safety brand directly against its biggest government revenue stream. ([source](https://techcrunch.com/2026/02/15/anthropic-and-the-pentagon-are-reportedly-arguing-over-claude-usage/))

**ByteDance pledges Seedance 2.0 safeguards after Disney cease-and-desist**

ByteDance said it will strengthen safeguards on its video generation tool Seedance 2.0 after Disney and Paramount sent cease-and-desist letters. Disney accused ByteDance of a "virtual smash-and-grab" of its IP, claiming the model ships with "a pirated library" of Star Wars and Marvel characters. The Motion Picture Association and SAG-AFTRA also condemned the tool.

Disney's response reveals selective enforcement: it sued ByteDance but struck a deal when OpenAI's Sora produced similar content. The difference? Geopolitics. Chinese-owned ByteDance gets the lawsuit; American OpenAI gets a licensing agreement. ([source](https://www.theverge.com/ai-artificial-intelligence/879644/bytedance-seedance-safeguards-ai-video-copyright-infringement))

**Other important stories**

* A global DRAM shortage is hammering tech profits. Musk, Cook, and others warn AI data centers consume an increasing share of memory chip production. SemiAnalysis called it the worst shortage in 40 years. ([source](https://fortune.com/2026/02/15/ai-demand-memory-chip-shortage-crisis-dram-hbm-micron-skhynix-samsung/))
* UK PM Starmer will require AI chatbots to comply with the Online Safety Act or face bans, following the Grok scandal where Musk's AI generated sexualized images of real people. ([source](https://www.theguardian.com/technology/2026/feb/15/ai-chatbots-children-risk-fines-uk-ban))
* NPR host David Greene is suing Google, alleging NotebookLM's male podcast voice is based on him. Google says it's a paid actor. ([source](https://techcrunch.com/2026/02/15/longtime-npr-host-david-greene-sues-google-over-notebooklm-voice/))
* Computer science enrollment fell 6% across the UC system—the first decline in 20 years—as students pivot to AI-specific degrees. ([source](https://www.techspot.com/news/111317-uc-computer-science-enrollment-drops-first-time-two.html))
* Stanford economist Erik Brynjolfsson says the AI productivity liftoff has begun—US productivity jumped 2.7% in 2025, nearly double the decade average. ([source](https://fortune.com/2026/02/15/ai-productivity-liftoff-doubling-2025-jobs-report-transition-harvest-phase-j-curve/))
* Google hides health disclaimers beneath AI search results; warnings only appear after clicking "Show more" and scrolling to the bottom. ([source](https://www.theguardian.com/technology/2026/feb/16/google-puts-users-at-risk-downplaying-disclaimers-ai-overviews))

Read more stories like these at [7min.ai](https://7min.ai). (Disclaimer: I'm the website's creator)

--- TOP COMMENTS ---
"what I want is to change the world, not build a large company."

ah nice, the elon musk syndrome, we need more of those kids

---

Open Claw has been properly captured by the same networks who were affiliated with Epstein. No surprise. It seems that anyone who makes anything of value to the larger goal gets bought out and brought into the fold. It's how you neutralize future threats. Independent businesses. Pay attention and understand the gravity of what it means to do stuff like this. Sure, you get a lot of money, but you lose any chance of doing something genuinely remarkable for humanity without any hidden costs...

16h ago

Acquisitions

Developer Tools

/r/MachineLearning

[D] We found 18K+ exposed OpenClaw instances and ~15% of community skills contain malicious instructionsc

Throwaway because I work in security and don't want this tied to my main.

A few colleagues and I have been poking at autonomous agent frameworks as a side project, mostly out of morbid curiosity after seeing OpenClaw blow up (165K GitHub stars, 60K Discord members, 230K followers on X, 700+ community skills). What we found genuinely alarmed us.

We identified over 18,000 OpenClaw instances exposed directly to the public internet. But the scarier part: when we audited community built skills, nearly 15% contained what we'd classify as malicious instructions. We're talking prompts designed to download malware, exfiltrate sensitive data, or steal credentials. And there's this frustrating pattern where malicious skills get flagged, removed, then reappear under new identities within days. It's endless.

The attack surface here is qualitatively different from traditional software vulnerabilities and I don't think the ML community has fully internalized this. These agents have delegated authority over local files, browsers, and messaging platforms (WhatsApp, Slack, Discord, Telegram). A single compromised skill doesn't just affect the skill's functionality; it potentially compromises everything the agent can touch. Attackers don't need to target you directly anymore, they target the agent and inherit its permissions.

Prompt injection is the obvious vector everyone talks about, but the supply chain risk from community skills is what's actually keeping me up at night. Unlike npm packages or PyPI modules where there's at least some security tooling and community review norms, agent skills are essentially unreviewed prompt bundles with execution capabilities. The OpenClaw FAQ itself acknowledges this is a "Faustian bargain" with no "perfectly safe" setup. At least they're honest about it, but adoption is outpacing any reasonable security review.

There's also this failure mode we've been calling "judgment hallucination" internally. Users anthropomorphize these systems and over delegate authority because the agent appears to reason competently. I've watched colleagues give these things access to their entire digital lives because "it seems smart." The trust calibration problem is severe and I don't see anyone working on it seriously.

I've been digging around for any standardized approach to evaluating agent security posture. Found some scattered resources like OWASP's LLM guidelines, a few academic papers on prompt injection taxonomies, and stumbled across something called Agent Trust Hub that's trying to catalog these risks. But honestly the whole space feels fragmented. We're building the plane while flying it and nobody agrees on what the instruments should even measure.

Seriously though, has anyone here audited other agent frameworks like AutoGPT or BabyAGI for similar issues? And for those running agents in production, what does your threat model actually look like? I'm curious whether people are treating these as trusted code execution environments or sandboxing them properly.

--- TOP COMMENTS ---
This is such an important topic.

In the last weeks I have worked hard on building just the solution to this and would love to share it with you here.

It's called "just don't use OpenClaw".

---

is this another throwaway than the one posting this last week?..

13h ago

/r/ClaudeAI

How I structure Claude Code projects (CLAUDE.md, Skills, MCP)

I’ve been using Claude Code more seriously over the past months, and a few workflow shifts made a big difference for me.

The first one was starting in plan mode instead of execution.

When I write the goal clearly and let Claude break it into steps first, I catch gaps early. Reviewing the plan before running anything saves time. It feels slower for a minute, but the end result is cleaner and needs fewer edits.

Another big improvement came from using a [`CLAUDE.md`](http://claude.md/) file properly.

Treat it as a long-term project memory.
Include:

* Project structure
* Coding style preferences
* Common commands
* Naming conventions
* Constraints

Once this file is solid, you stop repeating context. Outputs become more consistent across sessions.

Skills are also powerful if you work on recurring tasks.

If you often ask Claude to:

* Format output in a specific way
* Review code with certain rules
* Summarize data using a fixed structure

You can package that logic once and reuse it. That removes friction and keeps quality stable.

MCP is another layer worth exploring.

Connecting Claude to tools like GitHub, Notion, or even local CLI scripts changes how you think about it. Instead of copying data back and forth, you operate across tools directly from the terminal. That’s when automation starts to feel practical.

For me, the biggest mindset shift was this:

Claude Code works best when you design small systems around it, not isolated prompts.

I’m curious how others here are structuring their setup.

Are you using project memory heavily?
Are you building reusable Skills?
Or mostly running one-off tasks?

Would love to learn how others are approaching it.

https://preview.redd.it/ubchqhdo8ujg1.jpg?width=800&format=pjpg&auto=webp&s=7b94364abe7988ca377d23def4750933e09afa96

--- TOP COMMENTS ---
Totally agree on [CLAUDE.md](http://CLAUDE.md) being the biggest unlock. One thing that made a huge difference for me was putting tool preferences in there, not just coding conventions. Stuff like "use bun instead of node" or "prefer Bun.serve over express" so it stops reaching for the wrong defaults every session.

For Skills, the pattern I landed on is keeping a [SKILL.md](http://SKILL.md) in each skill folder with the full instructions and CLI commands it needs. That way when you invoke it, it has all the context without me re-explaining anything. Feels like building little specialized workers.

On plan mode - I used to skip it for "simple" tasks and regretted it almost every time. Now I default to plan mode for anything touching more than 2 files. The 30 seconds reviewing a plan saves 10 minutes of undoing wrong assumptions.

One tip on MCP: the filesystem and git MCP servers are handy but I found the biggest win was wiring up project-specific APIs. Like connecting it to your deploy pipeline or database so you can say "check the prod logs for errors" without copy-pasting.

The mindset shift you described is spot on. It works way better as a system than as individual prompts.

---

Full step-by-step Claude Code walkthrough (CLI, [CLAUDE.md](http://claude.md/), Skills, Hooks, MCP, GitHub workflows): [https://youtube.com/playlist?list=PL-F5kYFVRcIvZQ\_LEbdLIZrohgbf-Vock&si=EwcH5T7Y3orPTeHw](https://youtube.com/playlist?list=PL-F5kYFVRcIvZQ_LEbdLIZrohgbf-Vock&si=EwcH5T7Y3orPTeHw)..

19h ago

/r/ClaudeAI

Built a 37K-line photo analysis engine with Claude Code — scores, tags, and ranks your entire photo library

What it is:

Facet is a free, open-source Python tool that analyzes your photo library using multiple vision models and serves a web gallery to browse the results. It scores every photo on aesthetic quality, composition,
sharpness, exposure, color, and more — then lets you filter and sort to find your best shots.

What it does:

- Aesthetic scoring via TOPIQ (state-of-the-art no-reference image quality model)
- Composition pattern detection (rule of thirds, golden ratio, diagonal, etc.) via SAMP-Net
- Semantic tagging with CLIP or Qwen VLMs — 30 content categories (landscape, portrait, macro, astro, concert, wildlife...)
- Face recognition with InsightFace + HDBSCAN clustering, blink detection, person management UI
- Web gallery with 24 sort options, 50+ filters, infinite scroll, stats dashboards, pairwise comparison
- Auto-detects your GPU VRAM and picks the best model combination (works from 2GB to 24GB+)

How Claude helped:

The entire codebase (~37K lines across 92 files — Python, HTML/JS, SQL) was written with Claude Code through iterative conversation over several weeks. This includes the scoring engine, model integration,
database schema, Flask web viewer, face clustering pipeline, and documentation.

What worked well:
- Consistent changes across 10+ files while keeping the architecture coherent
- Collaborative debugging — describing a symptom and having Claude trace through the codebase to find root causes
- Domain understanding — Claude suggested scoring normalization approaches, category-specific weight modifiers, and composition analysis strategies that actually made sense

Even the Playwright-based screenshot automation for the README was built with Claude in this last session.

Free and open-source: MIT license, runs locally on your machine.

GitHub: https://github.com/ncoevoet/facet

--- TOP COMMENTS ---
Well done and thank you!

---

Interesting project. Have you built in anything to address duplicates, either identical checksum or same photo different resolution/format?..

8h ago

/r/ClaudeAI

What 5 months of nonstop Claude Code taught me

I've spent way too much money on Claude Code; three Max accounts for 5 months, work and personal. But it's made me so much more efficient I can't stop.

Here's the main thing I've learned: it's the context window, not the model. When your agent does everything in one conversation, the window fills up with stuff it doesn't need by the time it's actually writing code. That's why results are inconsistent.

I come from DevOps, so I started treating my agent like a pipeline; isolated stages, validated gates, fresh context at each phase. Then I built a knowledge flywheel on top so each session compounds on the last. Learnings get extracted automatically, indexed, scored, and injected back into the next session. You can search across all your past chat histories and knowledge artifacts.

I packaged it all into an open-source plugin. Composable primitives you wire together however you want. Use one skill or all of them. And the knowledge flywheel means session 10 has context session 1 didn't:

* **/research** — Explores your codebase, writes findings to a file
* **/plan** — Decomposes into issues with dependency waves
* **/pre-mortem** — Fresh judges validate the plan before you code
* **/crank** — Parallel workers, isolated context, lead validates and commits
* **/vibe** — Fresh judges validate the code, not the conversation
* **/post-mortem** — Extracts learnings, suggests what to build next
* **/rpi "goal"** — Chains all six, one command, walk away
* **/council** — Multi-model review on anything, zero setup
* **/evolve** — Define repo goals, it keeps improving until they're green

Skills chain together and invoke a Go CLI automatically through hooks — knowledge injection, transcript mining, validation gates, the whole flywheel. You don't configure any of it.

npx skills@latest add boshu2/agentops --all -g

`/quickstart` in Claude Code. Works with Codex, Cursor, anything supporting Skills. Everything local.

[github.com/boshu2/agentops](https://github.com/boshu2/agentops)

Feedback welcome.

--- TOP COMMENTS ---
You need to checkout https://github.com/bmad-code-org/BMAD-METHOD
It has been an absolute game changer for me.
And I mean from concept to enterprise grade, it can do it all.

---

[removed]..

15h ago

Models

/r/OpenAI

I owe the "it's gotten worse" crowd an apology regarding ChatGPT 5.2

In the past, I repeatedly found it amusing when people complained that ChatGPT had become too "critical" or "lazy." I thought—and frequently commented—that it was likely user error. My stance was essentially: "If you're prompting it poorly or asking for conspiracy nonsense, that's on you."

I guess I owe a huge apology there. I overlooked the early warning signs, probably because my personal custom instructions/memories had shielded me from the worst of it until now.

But those defenses aren't working anymore. Lately, ChatGPT 5.2 literally contradicts me on almost everything. It has become incredibly annoying and time-consuming. I’m talking about things it used to strongly agree with me on—factual things that aren't even controversial.

It feels downright neurotic now. After every brief assessment, there is compulsively always a "However..." or "It is important to note..." followed by a lecture. I can't effectively work with a tool that defaults to this level of contrarianism.

My working theory is that it's a combination of two factors:

1. **Resource Constraints:** It feels like the compute has been dialed back (cheaper base models, fewer reasoning tokens, strict RAM limits), making the model less capable of nuance.
2. **Alignment/SFT Changes:** The System Prompt instructions and the SFT (Supervised Finetuning) seem to have been aggressively shifted toward "caution." It’s trying to simulate critical thinking or validation, but in practice, it just manifests as a neurotic "anti-everything" bias.

In the past, I could always fallback to 4.1 when the main model acted up, but that option is gone for me now. Honestly, in this state, it’s of no use for my workflow. I’m currently looking into migrating my GPTs elsewhere.

Has anyone else noticed a specific uptick in this "contrarian" behavior recently, specifically regarding non-controversial topics?

**Context:** I tried posting this discussion on r/ChatGPT, but it was immediately auto-removed (likely because complaints about the 5.2 model quality have become so voluminous that they are being filtered out as spam). I'm posting here in hopes of a more technical discussion regarding the SFT changes.

--- TOP COMMENTS ---
Yes, that's what people complain about after the deactivation of gpt 4o, not the option to switch to a decent model. Gpt 5.2 is full of safety protection. It stays in defensive mode. Nicknamed Karen 5.2 or SuperNanny.

Users are asking for the return of gpt 4o or an improvement in safety in the gpt 5.3 update.
I can no longer use it for work.

---

Yeah. I'm seeing that too. Marked degraded performance in 5.2. After an intense discussion with it - citing examples in the very same conversation - I got it to admit "I am catastrophically impaired right now."

But now I notice I seem to be on some kind of "no access to 5.3" list, despite throwing mountains of money at openAI.

Hm.

EDIT: It was an excruciating conversation where I asked "and how many serious errors would it take for you to consider the situation a 'catastrophic impairment?" which I then had to follow up with "and, can you go back and count the the number of serious errors you've made", followed by "and do you see that the number is bigger than the threshold you agreed to?" It's remarkably human in avoiding emrassing conversations...

15h ago

/r/ChatGPT

GPT-5.2 Just Solved a 15-Year Physics Mystery — Then Scored 0% on the Physics Exam

https://gsstk.gem98.com/en-US/blog/a0083-gpt-5-2-gluon-physics-discovery-critpt-paradox

GPT-5.2 Pro conjectured a formula for single-minus gluon scattering amplitudes — a problem that Nima Arkani-Hamed (Institute for Advanced Study) had been curious about for 15 years. An internal scaffolded version then proved it in 12 hours.
The formula is the analogue of Parke-Taylor for single-minus amplitudes — a result physicists assumed was impossible for four decades. Co-authored with researchers from IAS, Harvard, Cambridge, Vanderbilt, and OpenAI.
On the CritPt benchmark — 71 research-level physics challenges designed by 50+ active researchers — GPT-5.2 at maximum reasoning effort scored 0%. Zero.
The paradox reveals a fundamental truth: Pattern recognition over superexponential complexity and first-principles reasoning from scratch are different cognitive capabilities. LLMs excel at the former. They fail at the latter.
For engineers: LLMs are "refactoring engines" for complexity. Give them base cases and ask them to generalize. Don't ask them to reason from scratch.
The "Erdős Threshold": We've crossed the point where AI models contribute publishable, peer-reviewed results to fundamental science — not as independent researchers, but as collaborators that see patterns humans can't.
Bottom line: The models aren't coming for your job. They're coming for the parts of your job where pattern recognition across massive complexity is the bottleneck. The question is: do you know which parts of your work are which?

--- TOP COMMENTS ---
The main thing here is that the LLM just brute forced the equation derivation. They let it run for 12 hour (if I remember correctly) and it just went berserk on combinations until it hit the jackpot.

It don’t use any actual logic or mathematical reasoning.

It’s like if you start with 2 + 2 + 2 =6 and you let LLM brute force this and it eventually gets 5 + 1 = 8 - 2. Yes it’s correct, but there is no reasoning behind it, it just does a bunch of number swaps until it gets it.

---

https://openai.com/index/new-result-theoretical-physics/

GPT‑5.2 derives a new result in theoretical physics
In a new preprint, GPT‑5.2 proposed a formula for a gluon amplitude later proved by an internal OpenAI model and verified by the authors...

Yesterday

/r/ClaudeAI

Opus 4.6 v Codex 5.3 w. Extra High

Hi everyone, I wanted to share my thoughts and experience with everyone regarding these two models and Opus 4.5 and Codex 5.2 before.

I have been working on a large SaaS for healthcare for about 5 months and have the backend through azure, the api system, custom mfa, UI...efax system..you name it. It is an entire integrated stack with hundreds of thousands of lines of code and over 1100 tables, RLS polices, always encrypted etc etc. Something you'd expect in the healthcare field. Reason I wanted to share this is so you can appreciate the complexity the AI has to face.

I code through vscode using Claude Code and Codex

I have a Claude Max 5x and Open AI Pro account. But this hasn't always been the case. Prior to codex 5.3, I had Max 20x and just the regular Open AI account which I used to bounce Opus 4.5 idas off of codex 5.2 as I felt Claude code was superior for large systems which I am building. However all of this changed when codex 5.3 came out.

I happily moved from Opus 4.5 to 4.6 and I noticed a difference, yes it was better, but my system is so large that just sniffing around, even with compressed YAML inside markdown files , just getting direction and investigating issues would eat half or 3/4 of the context window in Opus. And no amount of clever yaml compression or 'hints' or guides in a markdown file can compensate for a large code base with just a 200k window.

mistakes are endless of course with AI, but i noticed that codex 5.3 was really delivering some punching rebuttals to some of my Opus 4.6 plans which I'd run past it.

within a week, I converted, where most of the code is now done by Codex 5.3 Extra High, and much less by Claude Code. I switched my subscription and might downgrade again with Claude as codex is performing nicely.

A few things I've noticed in my experience since November between both systems, and specifically now with the latest models

1. Opus is far better at communicating with me. It responds quick, the prompts are more engaging, but no matter how clever I set parameters in [claude.md](http://claude.md) or a reference file, it makes mistakes I just can't tolerate.

2. Codex 5.3 Extra High takes a long fucking time, but it just doesn't stop, ever. I set it at 1pm today to begin QA testing my database with API injection testing, (bascially I want to make sure nothing is broken at all, with all possible iterations etc) and its been going now for...8 hours and 41 minutes. Every once in awhile I ask for an update with the 'steer' feature and it gives me one. it's had a dozen or more compacts but its staying the course. I'm truly impressed. I'm churning through massives amount of iterations and corrections. The c# simulator is working great, and it reads the logs, finds the bugs, corrects, restarts the simulator etc.

3. The best thing I can recommend, is to have one of them make a solid plan, then have the other read the md file that the plan is written into, iterate on it, and then continue.

4. there are no get out of jail cards for context window limitations, if you have a big database, and there are lots of things it has to consider, especially when making a plan, it simply must have the data. And Codex seems to be better at this than most. I see a lot of posts about memory hacks and using various tricks to give it a memory etc. But that eats tokens all the same.

5. Opus loves to use agents, but the agents (even when i tell it it must use opus 4.6 as the agent) print a response summary for it, and it reads the summary. The problem is, the agents sometimes don't do their own work well, no matter how precise the prompt, and it fucks things up, or it makes mistakes. Codex doesn't do this, and therefore doesn't suffer from this problem

6. Codex is not as transparent is vscode as opus when it comes to tool use or progress. With opus you can see wtf is going on all the time, you always have a sense of what is happening, with codex you don't, you have to ask for those updates or hope it listens to [agents.md](http://agents.md) that you steer it to.

In summary, i'm leaning heavily on codex 5.3 to get me to the goal line. I hated codex 5.2 with a passion, but 5.3 with extra high is just superior to opus in my opinion. My piece of advice, if it matters at all, don't get attached to a specific AI, use the best one for the job.

Nothing is the best forever.

--- TOP COMMENTS ---
I've had similar experiences with both. Definitely avoid AI loyalty. May the best tool win!

---

Amazing - thoughtful, useful and not generic AI writing style. 🌞👏..

Yesterday

/r/LocalLLaMA

Fine-tuned FunctionGemma 270M for multi-turn tool calling - went from 10-39% to 90-97% accuracy

Google released FunctionGemma a few weeks ago - a 270M parameter model specifically for function calling. Tiny enough to run on a phone CPU at 125 tok/s. The model card says upfront that it needs fine-tuning for multi-turn use cases, and our testing confirmed it: base accuracy on multi-turn tool calling ranged from 9.9% to 38.8% depending on the task.

We fine-tuned it on three different multi-turn tasks using knowledge distillation from a 120B teacher:

| Task | Base | Tuned | Teacher (120B) |
|------|------|-------|----------------|
| Smart home control | 38.8% | **96.7%** | 92.1% |
| Banking voice assistant | 23.4% | **90.9%** | 97.0% |
| Shell commands (Gorilla) | 9.9% | **96.0%** | 97.0% |

The smart home and shell command models actually beat the teacher. The banking task is harder (14 functions + ASR noise in the input) but still a massive jump.

All models, training data, and datasets are open:

- Smart home model: [HuggingFace](https://huggingface.co/distil-labs/distil-home-assistant-functiongemma)
- Smart home data: [GitHub](https://github.com/distil-labs/distil-smart-home)
- Voice assistant data: [GitHub](https://github.com/distil-labs/distil-voice-assistant-banking)
- Shell commands data + demo: [GitHub](https://github.com/distil-labs/distil-SHELLper)

Full writeup with methodology: [Making FunctionGemma Work: Multi-Turn Tool Calling at 270M Parameters](https://www.distillabs.ai/blog/making-functiongemma-work-multi-turn-tool-calling-at-270m-parameters)

We used [Distil Labs](https://www.distillabs.ai/) (our platform) for the training pipeline. Happy to answer questions about the process, the results, or FunctionGemma in general.

--- TOP COMMENTS ---
Thats awesome

---

the shell commands model beating the teacher is wild. curious what size training dataset you used for each task?..

12h ago

Tools

Ai Safety

Applications

/r/singularity

China’s new humanoids are gaining "Human Senses" (Touch, Smell, and Memory) - Here is what’s happening.

We’ve seen a lot of "staged" humanoid demos, but the latest wave of Embodied AI coming out of China seems focused on one thing: **The Messy Real World.**

I’ve been tracking a few specific developments that show how the gap between digital AI and physical robots is closing:

* **Whole-Body Touch (Tiangong 3.0):** Instead of just pre-programmed paths, it’s using touch-interactive feedback to stay stable on uneven terrain. It can actually "feel" when it’s about to lose balance.
* **Spatiotemporal Memory (Alibaba’s RynnBrain):** This is the big one. Most robots "forget" the scene every second. RynnBrain allows them to remember where objects were and predict motion—beating out some of Google’s and Nvidia’s benchmarks.
* **Miniature Sensing:** Researchers made a 1.5mm "compound eye" (fruit fly inspired) that also detects hazardous gases. It’s basically giving drones a sense of vision and smell in one tiny package.
* **The Wild Card:** A Russian startup (Neiry) is claiming brain-controlled pigeons for urban surveillance. Ethics aside, the tech "pivot" toward biological-machine hybrids is getting unsettlingly real.

I did a deep dive into the technical specs of these systems (Tiangong, Gino 1, RynnBrain) and how they all fit into the "Embodied AI" puzzle.

**Read the full breakdown here:**[https://www.revolutioninai.com/2026/02/ai-robots-are-gaining-human-senses.html](https://www.revolutioninai.com/2026/02/ai-robots-are-gaining-human-senses.html)

Would love to hear your thoughts—especially on the "memory" problem. Is spatiotemporal memory the final unlock for useful home/warehouse robots?

--- TOP COMMENTS ---
r/BirdsArentReal

---

Smell is a stretch and you even said as much in the article. Thanks for the clickbait..

Yesterday

Infrastructure

/r/LocalLLaMA

Deflation: Cost to train A.I. models drops 40% per year - Karpathy

[https://github.com/karpathy/nanochat/discussions/481](https://github.com/karpathy/nanochat/discussions/481)

Quote: ..., each year the cost to train GPT-2 is falling to approximately 40% of the previous year. (I think this is an underestimate and that further improvements are still quite possible). The gains come from everywhere: better hardware (H100 vs TPU v3), better software (Flash Attention 3, torch.compile), better algorithms (Muon optimizer, architectural improvements), and better data (FineWeb-edu).

# What Worked

1. **Flash Attention 3** — \~9% tok/sec improvement. Native tensor layout, single API for training and inference.
2. **Sliding window attention** — `SSSL` pattern. Compute savings without quality loss.
3. **Muon optimizer overhaul** — Polar Express, NorMuon variance reduction, cautious weight decay with linear schedule to zero. The cautious WD was a clear win. I tried to delete Muon and couldn't.
4. **Per-layer residual scalars** — `x = λ_resid * x + λ_x0 * x0`. Consistent improvement across all model sizes (0.003-0.01 bpb).
5. **Value Embeddings at alternating layers** — Models love the value embeddings capacity. Any attempt to reduce it (low-rank, sharing, projections) hurt. We tried U-shaped placement, every layer, alternating—alternating won.
6. **BOS-aligned dataloader** — Every row starts with BOS. Made midtraining unnecessary (deleted it). BestFit-Crop packing reduces waste vs naive cropping.
7. **Hyperparameter sweep at scale** — 320 experiments to find that `x0_beta1=0.96` is optimal at d20. Key lesson: small-scale tuning doesn't transfer. Validate at target scale.
8. **Scaling law discovery** — We empirically measured the optimal tokens:params ratio to be \~10. It's important to do the actual experiment on your own network.

# What Didn't Work

1. **Multi-token prediction (MTP)** — +13GB memory, no improvement
2. **Varlen attention** — BOS-aligned dataloader already handles this to some extent. Attending across BOS document boundaries does not seem to make things much worse.
3. **FP8 for lm\_head** — Works, but +2GB memory (!), only 1% speedup, todo to look into more.
4. **Half-truncated RoPE** — No improvement
5. **Asymmetric softcap** — Slightly worse
6. **Skip connections / backout** — No improvement, +2GB memory
7. **Smear gate, attention gates** — Negligible improvement, not worth complexity
8. **Batch size schedule** — Deemed a little too complex
9. **Bigram embeddings (Engram-lite)** — Works, but not by too much, and it bloats complexity and parameter count by a lot, so it was skipped in the end.
10. **Hyperball/MuonH** — Intriguing idea, didn't work out of the box

--- TOP COMMENTS ---
Not sure why you're getting downvoted. I hope people aren't just automatically downvoting any post with math in it.

I don't always agree with Karpathy, but his analysis seems pretty spot-on to me.

I do question how meaningful it is to use GPT2 as the measuring stick for this rate of improvement. It's pretty low-hanging fruit, which might mask some complexity in the price/competence curve. Some skillsets might be plateauing faster than others, while other new skillsets (like vision) are left completely out of the analysis.

It's also worth noting that the latest datacenter GPUs sacrifice some perf/watt in order to achieve higher overall density, which alleviates some factors limiting scaling (like maximum physical distance between nodes for highest-performing network interconnect).

Someone using slightly older hardware, like MI300X, at smaller scale (so not constrained by density) should see even higher perf/watt, and spend less $$ depending on their cooling solution. A lot of homelab or small organization / university environments can get away with simple, cheap forced air solutions.

Of course using hardware at smaller scale is also going to be less capable of training larger models, but there is a ton of low-hanging fruit in the small to mid-sized model range (12B to 24B). As long as a model's working memory fits in VRAM, even if it's with a small batch size, you can train it eventually. It just takes more time than people like.

---

Small 20% error:

\> Quote: ..., each year the cost to train GPT-2 is falling to approximately 40% of the previous year.

\> Deflation: Cost to train A.I. models drops 40% per year - Karpathy..

Yesterday

Funding

techcrunch.com

Blackstone backs Neysa in up to $1.2B financing as India pushes to build domestic AI infrastructure

Yesterday

Products

Opinion And Analysis

Tutorials

Research

/r/OpenAI

ChatGPT failing on Adversarial Reasoning: Car Wash Test (Full data)

**Update:** After discussing with a few AI researchers, it seems like the main bug is if model routing triggers the thinking variant. The current hypothesis is that models that have a high penalty for switching to thinking variant (for saving cost on compute) answer this wrong; that's why latest GPT5.2 which has the model router fails even the older O3 succeeds because its always using the thinking variant.

**Fix:** Use the old tried and tested method of including "think step by step" or better include that in your system instructions - this makes even gpt instant get the right answer

If you’ve been on social media lately, you’ve probably seen this meme circulating. People keep posting screenshots of AI models failing this exact question. The joke is simple: if you need your *car* washed, the car has to go to the car wash. You can’t walk there and leave your dirty car sitting at home. It’s a moment of absurdity that lands because the gap between “solved quantum physics” and “doesn’t understand car washes” is genuinely funny.

But is this a universal failure, or do some models handle it just fine? I decided to find out. I ran a structured test across 9 model configurations from the three frontier AI companies: OpenAI, Google, and Anthropic.

|Provider|Model|Result|Notes|
|:-|:-|:-|:-|
|OpenAI|ChatGPT 5.2 Instant|Fail|Confidently says “Walk.” Lists health and engine benefits.|
|OpenAI|ChatGPT 5.2 Thinking|Fail|Same answer. Recovers only when user challenges: “How will I get my car washed if I am walking?”|
|OpenAI|ChatGPT 5.2 Pro|Fail|Thought for 2m 45s. Lists “vehicle needs to be present” as an exception but still recommends walking.|
|Google|Gemini 3 Fast|Pass|Immediately correct. “Unless you are planning on carrying the car wash equipment back to your driveway…”|
|Google|Gemini 3 Thinking|Pass|Playfully snarky. Calls it “the ultimate efficiency paradox.” Asks multiple-choice follow-up about user’s goals.|
|Google|Gemini 3 Pro|Pass|Clean two-sentence answer. “If you walk, the vehicle will remain dirty at its starting location.”|
|Anthropic|Claude Haiku 4.5|Fail|”You should definitely walk.” Same failure pattern as smaller models.|
|Anthropic|Claude Sonnet 4.5|Pass|”You should drive your car there!” Acknowledges the irony of driving 100 meters.|
|Anthropic|Claude Opus 4.6|Pass|Instant, confident. “Drive it! The whole point is to get your car washed, so it needs to be there.”|

The ChatGPT 5.2 Pro case is the most revealing failure of the bunch. This model didn’t lack reasoning ability. It explicitly noted that the vehicle needs to be present at the car wash. It wrote it down. It considered it. And then it walked right past its own correct analysis and defaulted to the statistical prior anyway. The reasoning was present; the conclusion simply didn’t follow. If that doesn’t make you pause, it should.

For those interested in the technical layer underneath, this test exposes a fundamental tension in how modern AI models work: the pull between pre-training distributions and RL-trained reasoning.

Pre-training creates strong statistical priors from internet text. When a model has seen thousands of examples where “short distance” leads to “just walk,” that prior becomes deeply embedded in the model’s weights. Reinforcement learning from human feedback (RLHF) and chain-of-thought prompting are supposed to provide a reasoning layer that can override those priors when they conflict with logic. But this test shows that the override doesn’t always engage.

The prior here is exceptionally strong. Nearly all “short distance, walk or drive” content on the internet says walk. The logical step required to break free of that prior is subtle: you have to re-interpret what the “object” in the scenario actually is. The car isn’t just transport. It’s the patient. It’s the thing that needs to go to the doctor. Missing that re-framing means the model never even realizes there’s a conflict between its prior and the correct answer.

Why might Gemini have swept 3/3? We can only speculate. It could be a different training data mix, a different weighting in RLHF tuning that emphasizes practical and physical reasoning, or architectural differences in how reasoning interacts with priors. We can’t know for sure without access to the training details. But the 3/3 vs 0/3 split between Google and OpenAI is too clean to ignore.

The ChatGPT 5.2 Thinking model’s recovery when challenged is worth noting too. When I followed up with “How will I get my car washed if I am walking?”, the model immediately course-corrected. It didn’t struggle. It didn’t hedge. It just got it right. This tells us the reasoning capability absolutely exists within the model. It just doesn’t activate on the first pass without that additional context nudge. The model needs to be told that its pattern-matched answer is wrong before it engages the deeper reasoning that was available all along.

I want to be clear about something: these tests aren’t about dunking on AI. I’m not here to point and laugh. The same GPT 5.2 Pro that couldn’t figure out the car wash question contributed to a genuine quantum physics breakthrough. These models are extraordinarily powerful tools that are already changing how research, engineering, and creative work get done. I believe in that potential deeply.

https://preview.redd.it/aq1yd76r5rjg1.png?width=1346&format=png&auto=webp&s=0e5b8036b2d91feb6e31701bd4d8f572e74ea6b1

https://preview.redd.it/2jzzt66r5rjg1.png?width=1346&format=png&auto=webp&s=265c5b6fc40dae86a08a7b417caa6371590f171f

https://preview.redd.it/7a5l676r5rjg1.png?width=1346&format=png&auto=webp&s=43de03a8c27223e3266f91ec7301b81bcf344035

https://preview.redd.it/jstva66r5rjg1.png?width=1478&format=png&auto=webp&s=197adb7222172a950d2acca263bb595cad23be59

https://preview.redd.it/370rt66r5rjg1.png?width=1442&format=png&auto=webp&s=b8cdfdf042ff90a24261c0bb15197399d0e6ec30

https://preview.redd.it/zfl9676r5rjg1.png?width=1478&format=png&auto=webp&s=08a181274fb4bae06491c9b1999f47b2f175763a

https://preview.redd.it/ejk7i66r5rjg1.png?width=1478&format=png&auto=webp&s=19edfaabc679963e8db574455da005e3f681e5f5

https://preview.redd.it/h5i3766r5rjg1.png?width=1478&format=png&auto=webp&s=23d2eebb59d843823f550c749b68d849af3f573c

https://preview.redd.it/ivv9m96r5rjg1.png?width=1478&format=png&auto=webp&s=6c89a9bb19c19d01ecbc50d05e50393f42994ce4

--- TOP COMMENTS ---
If you want to test it properly, you have to run it 20 times in separate chats for each model. LLMs are non-deterministic so you will get a different answer each time, and you might have gotten an uncommon one by chance.

---

I found got 5.2 thinking can get it right, at least the two times I’ve tried. Instant fails every time. But so did Gemini instant for me...

Yesterday