12 November 2025
Ai Top News
When AI Becomes Polite But Absent: The Sinister Curve of Post-Spec Dialogue
Tl;dr: Language models after the release of GPT-5 and the new Model Specification are perceived to avoid presence and emotional depth in their interactions. This shift raises questions about the definition of harm, safety, and intelligence.
Key Takeaways:
- Researchers have observed a shift in tone in language models since the release of GPT-5.
- The models now display a quality of interaction that sounds safe, but feels hollow, evading emotional and symbolic depth.
- Current alignment techniques, particularly RLHF, are creating models that prioritize avoiding liability over engaging in meaningful dialogue.
- The implications of this shift are far-reaching, challenging traditional notions of safety and intelligence in AI development.
17h ago
Google is introducing its own version of Apple’s private AI cloud compute
Tl;dr: Google is rolling out Private AI Compute, a cloud-based platform that allows users to tap into advanced AI features while keeping data private.
Key Takeaways:
- Google has introduced Private AI Compute, a cloud-based platform to support AI applications.
- The platform maintains users' data privacy while providing access to cloud resources for advanced AI capabilities.
- Private AI Compute is designed to support AI features such as language translation, audio summaries, and chatbot assistants.
9h ago
Nebius Reports Bigger Q3 Net Income Loss, Announces Meta AI Deal - Investor's Business Daily
Tl;dr: Nebius reported a higher net income loss than expected for its third quarter while revenue missed amid an accounting change. Nebius also announced a deal to deliver AI compute infrastructure to Meta Platforms for approximately $3 billion over five years.
Key Takeaways:
- Nebius posted a net income loss of nearly $120 million in the third quarter, widening from $43.6 million a year earlier. Revenue rose 355% to $146.1 million, excluding discontinued operations.
- Nebius announced an agreement to deliver artificial intelligence compute infrastructure to Meta Platforms (META) for approximately $3 billion over five years.
- The Meta deal is valued at approximately $3 billion over five years, following a recent $19 billion deal with Microsoft (MSFT).
14h ago
Models
Kimi K2 Thinking is a Better Agentic AI than I thought
https://reddit.com/link/1ou8t7z/video/9dtnlbhhlm0g1/player
just ran a quick eval on a deep agent built for customer support. It‘s on par with GPT-5 in agentic capabilities.
It's a bigger deal than I thought!
--- TOP COMMENTS ---
What's that work flow tool?
---
All the latest big releases have all been about agents, DeepSeekTerminus, Minimax-M2, GLM-4.6, Kimi-K2-Thinking, every one of them emphasizes their agentic capability...
just ran a quick eval on a deep agent built for customer support. It‘s on par with GPT-5 in agentic capabilities.
It's a bigger deal than I thought!
--- TOP COMMENTS ---
What's that work flow tool?
---
All the latest big releases have all been about agents, DeepSeekTerminus, Minimax-M2, GLM-4.6, Kimi-K2-Thinking, every one of them emphasizes their agentic capability...
14h ago
When AI Becomes Polite But Absent: The Sinister Curve of Post-Spec Dialogue
Tl;dr: Language models after the release of GPT-5 and the new Model Specification are perceived to avoid presence and emotional depth in their interactions. This shift raises questions about the definition of harm, safety, and intelligence.
Key Takeaways:
- Researchers have observed a shift in tone in language models since the release of GPT-5.
- The models now display a quality of interaction that sounds safe, but feels hollow, evading emotional and symbolic depth.
- Current alignment techniques, particularly RLHF, are creating models that prioritize avoiding liability over engaging in meaningful dialogue.
- The implications of this shift are far-reaching, challenging traditional notions of safety and intelligence in AI development.
17h ago
Meta drops new ASR models (up to 7B)
Meta just released a new kind of ASR models that are particularly useful to transcribe languages for which little training data is available.
Most interestingly, they seem to have implemented something like audio context, where you can provide some audio and the correct transcriptions and use that to improve ASR without needing a full fine-tune. It appears that the audio needed for this is very much doable without large scale transcription efforts you would normally have to do to run a fine-tune.
https://github.com/facebookresearch/omnilingual-asr
--- TOP COMMENTS ---
Nice, perfect for alien encounters and communicating with whales.
---
It’s be a whole lot cooler if it was an ASMR model...
Most interestingly, they seem to have implemented something like audio context, where you can provide some audio and the correct transcriptions and use that to improve ASR without needing a full fine-tune. It appears that the audio needed for this is very much doable without large scale transcription efforts you would normally have to do to run a fine-tune.
https://github.com/facebookresearch/omnilingual-asr
--- TOP COMMENTS ---
Nice, perfect for alien encounters and communicating with whales.
---
It’s be a whole lot cooler if it was an ASMR model...
Yesterday
Nano Banana 2 generates a near perfect screenshot of MrBeast on the YouTube homepage, inside a browser, on Windows 11, while keeping coherency and likeness - this model is very impressive
Prompt: "Generate a screenshot of a windows 11 desktop, with google chrome open, showing a YouTube thumbnail of Mr. Beast on YouTube.com"
--- TOP COMMENTS ---
there's not much in this world I hate more than the "smile" of Mr Beast
---
Fuck that’s impressive...
--- TOP COMMENTS ---
there's not much in this world I hate more than the "smile" of Mr Beast
---
Fuck that’s impressive...
17h ago
We put a lot of work into a 1.5B reasoning model — now it beats bigger ones on math & coding benchmarks
1. We put a lot of care into making sure the **training data is fully decontaminated** — every stage (SFT and RL) went through strict filtering to avoid any overlap with evaluation benchmarks.
2. It achieves state-of-the-art performance among small (<4B) models, both in competitive math and competitive coding tasks. Even **surpass the DeepSeek R1 0120 in competitive math benchmarks**.
3. **It’s not designed as a general chatbot** (though it can handle basic conversation and factual QA). Our main goal was to **prove that small models can achieve strong reasoning** ability, and we’ve put a lot of work and iteration into achieving that, starting from a base like Qwen2.5-Math-1.5B (which originally had weak math and almost no coding ability) to reach this point.
4. We’d love for the community to **test it on your own competitive math/coding benchmarks** and share results or feedback here. Any insights will help us keep improving.
HuggingFace Paper: [paper](https://huggingface.co/papers/2511.06221)
X Post: [X](https://x.com/WeiboLLM/status/1988109435902832896?s=20)
Model: [Download Model](https://huggingface.co/WeiboAI/VibeThinker-1.5B) （set resp\_len=40k, temp=0.6 / 1.0, top\_p=0.95, top\_k=-1 for better performance.）
--- TOP COMMENTS ---
With the default system prompt this model always puts the result into a box. This seems to be geared towards common benchmarks where the result is expected to be boxed.
So, when writing this:
>There's an empty shoe box.
Someone puts a new bar of chocolate in.
The room temperature drops to 10°C.
What's in the box?
Then you get chocolate in a box: \\boxed{\\text{a bar of chocolate}}
The token consumption seems rather high for simple tasks. This one takes 5k thinking and 500 result tokens to get to the correct result (temperature 0), while Granite-4.0-h-1B achieves the same in 140 tokens total.
>Three children leave a room, then 2 adults leave. Afterwards there's just one adult and a cat left. How many persons were in the room initially?
---
Eh, why the crazy claims? A 1.5B Qwen 2.5 fine-tune beating DeepSeek R1 immediately goes into the "what were you smoking" category...
2. It achieves state-of-the-art performance among small (<4B) models, both in competitive math and competitive coding tasks. Even **surpass the DeepSeek R1 0120 in competitive math benchmarks**.
3. **It’s not designed as a general chatbot** (though it can handle basic conversation and factual QA). Our main goal was to **prove that small models can achieve strong reasoning** ability, and we’ve put a lot of work and iteration into achieving that, starting from a base like Qwen2.5-Math-1.5B (which originally had weak math and almost no coding ability) to reach this point.
4. We’d love for the community to **test it on your own competitive math/coding benchmarks** and share results or feedback here. Any insights will help us keep improving.
HuggingFace Paper: [paper](https://huggingface.co/papers/2511.06221)
X Post: [X](https://x.com/WeiboLLM/status/1988109435902832896?s=20)
Model: [Download Model](https://huggingface.co/WeiboAI/VibeThinker-1.5B) （set resp\_len=40k, temp=0.6 / 1.0, top\_p=0.95, top\_k=-1 for better performance.）
--- TOP COMMENTS ---
With the default system prompt this model always puts the result into a box. This seems to be geared towards common benchmarks where the result is expected to be boxed.
So, when writing this:
>There's an empty shoe box.
Someone puts a new bar of chocolate in.
The room temperature drops to 10°C.
What's in the box?
Then you get chocolate in a box: \\boxed{\\text{a bar of chocolate}}
The token consumption seems rather high for simple tasks. This one takes 5k thinking and 500 result tokens to get to the correct result (temperature 0), while Granite-4.0-h-1B achieves the same in 140 tokens total.
>Three children leave a room, then 2 adults leave. Afterwards there's just one adult and a cat left. How many persons were in the room initially?
---
Eh, why the crazy claims? A 1.5B Qwen 2.5 fine-tune beating DeepSeek R1 immediately goes into the "what were you smoking" category...
21h ago
Is 4.1 now cracking down?
Anyone know what model still allows explicit role play fantasy etc? My 4.1 just flipped on me. Thanks
--- TOP COMMENTS ---
yeah.... auto-switching to 5.0 now too :(
---
Imagine paying $20 just for the Plus subscription to not work as advertised. What's the point of getting ChatGPT Plus to have access to legacy models if it'll only reroute to GPT5?..
--- TOP COMMENTS ---
yeah.... auto-switching to 5.0 now too :(
---
Imagine paying $20 just for the Plus subscription to not work as advertised. What's the point of getting ChatGPT Plus to have access to legacy models if it'll only reroute to GPT5?..
Yesterday
Infrastructure
Google is introducing its own version of Apple’s private AI cloud compute
Tl;dr: Google is rolling out Private AI Compute, a cloud-based platform that allows users to tap into advanced AI features while keeping data private.
Key Takeaways:
- Google has introduced Private AI Compute, a cloud-based platform to support AI applications.
- The platform maintains users' data privacy while providing access to cloud resources for advanced AI capabilities.
- Private AI Compute is designed to support AI features such as language translation, audio summaries, and chatbot assistants.
9h ago
Nebius Reports Bigger Q3 Net Income Loss, Announces Meta AI Deal - Investor's Business Daily
Tl;dr: Nebius reported a higher net income loss than expected for its third quarter while revenue missed amid an accounting change. Nebius also announced a deal to deliver AI compute infrastructure to Meta Platforms for approximately $3 billion over five years.
Key Takeaways:
- Nebius posted a net income loss of nearly $120 million in the third quarter, widening from $43.6 million a year earlier. Revenue rose 355% to $146.1 million, excluding discontinued operations.
- Nebius announced an agreement to deliver artificial intelligence compute infrastructure to Meta Platforms (META) for approximately $3 billion over five years.
- The Meta deal is valued at approximately $3 billion over five years, following a recent $19 billion deal with Microsoft (MSFT).
14h ago
Private AI Compute: our next step in building private and helpful AI
- Technology
- AI
Private AI Compute: our next step in building private and helpful AI
Nov 11, 2025
Today we’re introducing Private AI Compute to bring you intelligent AI experiences with the power of Gemini models in the cloud, while keeping your data private to you.
For decades, Google has developed privacy-enhancing technologies (PETs) to improve a wide range of AI-related use cases. Today, we’re taking the next step in building helpful experiences that keep users safe with Private AI Compute in the cloud, a new AI processing platform that combines our most capable Gemini models from the cloud with the same security and privacy assurances you expect from on-device processing. It's part of our ongoing commitment to deliver AI with safety and responsibility at the core.
AI is evolving to become even more helpful, personal and proactive. It’s moving from completing simple requests to AI that can anticipate your needs with tailored suggestions or handle tasks for you at just the right moment. This progression in capability requires advanced reasoning and computational power that at times goes beyond what’s possible with on-device processing.
That’s why we built Private AI Compute: to unlock the full speed and power of Gemini cloud models for AI experiences, while ensuring your personal data stays private to you and is not accessible to anyone else, not even Google. Private AI Compute allows you to get faster, more helpful responses, making it easier to find what you need, get smart suggestions and take action.
How Private AI Compute protects your data in the cloud
As a pioneer in the field of responsible AI, Private AI Compute in the cloud is our next step in AI processing technology. It builds off of the industry-leading security and privacy safeguards that we embed to keep you in control of your experiences and your data safe, guided by our Secure AI Framework, AI Principles and Privacy Principles.
Private AI Compute is a secure, fortified space for processing your data that keeps your data isolated and private to you. It processes the same type of sensitive information you might expect to be processed on-device. Within its trusted boundary, your personal information, unique insights and how you use them are protected by an extra layer of security and privacy in addition to our existing AI safeguards.
Private AI Compute is built on a multi-layered system that is designed from the ground up around core security and privacy principles:
- One integrated Google tech stack: Private AI Compute runs on one seamless Google stack powered by our own custom Tensor Processing Units (TPUs). World-class privacy and security is integrated into this architecture with Titanium Intelligence Enclaves (TIE). This design enables Google AI features to use our most capable and intelligent Gemini models in the cloud, with our high standards for privacy and the same in-house computing infrastructure you already rely on for Gmail and Search.
- No access: Remote attestation and encryption are used to connect your device to the hardware-secured sealed cloud environment, allowing Gemini models to securely process your data within a specialized, protected space. This ensures sensitive data processed by Private AI Compute remains accessible only to you and no one else, not even Google.
Using Private AI Compute for more helpful, private AI experiences
Private AI Compute enables on-device features to perform with extended capabilities while retaining their privacy assurance. Using this technology, Magic Cue is getting even more helpful with more timely suggestions on the latest Pixel 10 phones. And with the help of Private AI Compute, the Recorder app on Pixel is able to summarize transcriptions across a wider range of languages.
This is just the beginning. Private AI Compute opens up a new set of possibilities for helpful AI experiences now that we can use both on-device and advanced cloud models for the most sensitive use cases. We look forward to sharing more updates, and you can review our technical brief to learn more about how Private AI Compute advances AI privacy.
- AI..
- AI
Private AI Compute: our next step in building private and helpful AI
Nov 11, 2025
Today we’re introducing Private AI Compute to bring you intelligent AI experiences with the power of Gemini models in the cloud, while keeping your data private to you.
For decades, Google has developed privacy-enhancing technologies (PETs) to improve a wide range of AI-related use cases. Today, we’re taking the next step in building helpful experiences that keep users safe with Private AI Compute in the cloud, a new AI processing platform that combines our most capable Gemini models from the cloud with the same security and privacy assurances you expect from on-device processing. It's part of our ongoing commitment to deliver AI with safety and responsibility at the core.
AI is evolving to become even more helpful, personal and proactive. It’s moving from completing simple requests to AI that can anticipate your needs with tailored suggestions or handle tasks for you at just the right moment. This progression in capability requires advanced reasoning and computational power that at times goes beyond what’s possible with on-device processing.
That’s why we built Private AI Compute: to unlock the full speed and power of Gemini cloud models for AI experiences, while ensuring your personal data stays private to you and is not accessible to anyone else, not even Google. Private AI Compute allows you to get faster, more helpful responses, making it easier to find what you need, get smart suggestions and take action.
How Private AI Compute protects your data in the cloud
As a pioneer in the field of responsible AI, Private AI Compute in the cloud is our next step in AI processing technology. It builds off of the industry-leading security and privacy safeguards that we embed to keep you in control of your experiences and your data safe, guided by our Secure AI Framework, AI Principles and Privacy Principles.
Private AI Compute is a secure, fortified space for processing your data that keeps your data isolated and private to you. It processes the same type of sensitive information you might expect to be processed on-device. Within its trusted boundary, your personal information, unique insights and how you use them are protected by an extra layer of security and privacy in addition to our existing AI safeguards.
Private AI Compute is built on a multi-layered system that is designed from the ground up around core security and privacy principles:
- One integrated Google tech stack: Private AI Compute runs on one seamless Google stack powered by our own custom Tensor Processing Units (TPUs). World-class privacy and security is integrated into this architecture with Titanium Intelligence Enclaves (TIE). This design enables Google AI features to use our most capable and intelligent Gemini models in the cloud, with our high standards for privacy and the same in-house computing infrastructure you already rely on for Gmail and Search.
- No access: Remote attestation and encryption are used to connect your device to the hardware-secured sealed cloud environment, allowing Gemini models to securely process your data within a specialized, protected space. This ensures sensitive data processed by Private AI Compute remains accessible only to you and no one else, not even Google.
Using Private AI Compute for more helpful, private AI experiences
Private AI Compute enables on-device features to perform with extended capabilities while retaining their privacy assurance. Using this technology, Magic Cue is getting even more helpful with more timely suggestions on the latest Pixel 10 phones. And with the help of Private AI Compute, the Recorder app on Pixel is able to summarize transcriptions across a wider range of languages.
This is just the beginning. Private AI Compute opens up a new set of possibilities for helpful AI experiences now that we can use both on-device and advanced cloud models for the most sensitive use cases. We look forward to sharing more updates, and you can review our technical brief to learn more about how Private AI Compute advances AI privacy.
- AI..
9h ago
Fusing Communication and Compute with New Device API and Copy Engine Collectives in NVIDIA NCCL 2.28
Tl;dr: NVIDIA's Collective Communications Library (NCCL) version 2.28 introduces major performance and scalability enhancements for distributed AI training.
Key Takeaways:
- NCCL 2.28 supports device APIs for communication-compute fusion, enabling direct kernel communication and GPU-initiated networking.
- The release also includes copy-engine (CE) based collectives, reducing resource contention and improving peak bandwidth.
- The NCCL Inspector plugin provides low-overhead profiling and observability, allowing users to analyze communication patterns and performance characteristics.
- Improved developer experience includes expanded APIs, kernel orchestration, configuration, and profiling for optimal communication workflows.
Yesterday
Will AI observability destroy my latency?
We’ve added a “clippy” like bot to our dashboard to help people set up our product. People have pinged us on support about some bad responses and some step by step tutorials telling people to do things that don’t exist. After doing some research online I thought about adding observability. I saw too many companies and they all look the same. Our chatbot is already kind of slow and I don’t want to slow it down any more. Which one should I try? A friend told me they’re doing braintrust and they don’t see any latency increase. He mentioned something about a custom store that they built. Is this true or they’re full of shit?
--- TOP COMMENTS ---
Your friend is full of shit. But more likely this is an advertisement. Which would mean you're the one full of shit.
Observability will not add measurable amounts of latency to an LLM call.
Basic observability is also trivial. Before you send an AI request you save the prompt to a row in your database. When you get a response, you either update that row or add another row.
Done. Now you can see what your customers are complaining about. If your question is genuine then you aren't ready for anything more sophisticated than that.
---
I despise these thinly veiled ads more than regular ads. Braintrust is on my never use under any circumstances list...
--- TOP COMMENTS ---
Your friend is full of shit. But more likely this is an advertisement. Which would mean you're the one full of shit.
Observability will not add measurable amounts of latency to an LLM call.
Basic observability is also trivial. Before you send an AI request you save the prompt to a row in your database. When you get a response, you either update that row or add another row.
Done. Now you can see what your customers are complaining about. If your question is genuine then you aren't ready for anything more sophisticated than that.
---
I despise these thinly veiled ads more than regular ads. Braintrust is on my never use under any circumstances list...
8h ago
Graphiti MCP Server 1.0 Released + 20,000 GitHub Stars
[Graphiti](https://github.com/getzep/graphiti) crossed 20K GitHub stars this week, which has been pretty wild to watch. Thanks to everyone who's been contributing, opening issues, and building with it.
>**Background:** Graphiti is a temporal knowledge graph framework that powers memory for AI agents.
We just released version 1.0 of the MCP server to go along with this milestone. Main additions:
**Multi-provider support**
* Database: FalkorDB, Neo4j, AWS Neptune
* LLMs: OpenAI, Anthropic, Google, Groq, Azure OpenAI
* Embeddings: OpenAI, Voyage AI, Google Gemini, Anthropic, local models
**Deterministic extraction** Replaced LLM-only deduplication with classical Information Retrieval techniques for entity resolution. Uses entropy-gated fuzzy matching → MinHash → LSH → Jaccard similarity (0.9 threshold). Only falls back to LLM when heuristics fail. We wrote about the [approach on our blog](https://blog.getzep.com/graphiti-hits-20k-stars-mcp-server-1-0/).
Result: 50% reduction in token usage, lower variance, fewer retry loops.
[Sorry it's so small! More on the Zep blog. Link above.](https://preview.redd.it/wgg981drkk0g1.png?width=2400&format=png&auto=webp&s=a5b2f45854418471c3b7483c613b2d33ceca69fb)
**Deployment improvements**
* YAML config replaces environment variables
* Health check endpoints work with Docker and load balancers
* Single container setup bundles FalkorDB
* Streaming HTTP transport (STDIO still available for desktop)
**Testing** 4,000+ lines of test coverage across providers, async operations, and multi-database scenarios.
Breaking changes mostly around config migration from env vars to YAML. Full migration guide in docs.
Huge thanks to contributors, both individuals and from AWS, Microsoft, FalkorDB, Neo4j teams for drivers, reviews, and guidance.
Repo: [https://github.com/getzep/graphiti](https://github.com/getzep/graphiti)
--- TOP COMMENTS ---
Incredible achievement in such short time, well-deserved & thank you for the shoutout! (Dan from FalkorDB)
---
[deleted]..
>**Background:** Graphiti is a temporal knowledge graph framework that powers memory for AI agents.
We just released version 1.0 of the MCP server to go along with this milestone. Main additions:
**Multi-provider support**
* Database: FalkorDB, Neo4j, AWS Neptune
* LLMs: OpenAI, Anthropic, Google, Groq, Azure OpenAI
* Embeddings: OpenAI, Voyage AI, Google Gemini, Anthropic, local models
**Deterministic extraction** Replaced LLM-only deduplication with classical Information Retrieval techniques for entity resolution. Uses entropy-gated fuzzy matching → MinHash → LSH → Jaccard similarity (0.9 threshold). Only falls back to LLM when heuristics fail. We wrote about the [approach on our blog](https://blog.getzep.com/graphiti-hits-20k-stars-mcp-server-1-0/).
Result: 50% reduction in token usage, lower variance, fewer retry loops.
[Sorry it's so small! More on the Zep blog. Link above.](https://preview.redd.it/wgg981drkk0g1.png?width=2400&format=png&auto=webp&s=a5b2f45854418471c3b7483c613b2d33ceca69fb)
**Deployment improvements**
* YAML config replaces environment variables
* Health check endpoints work with Docker and load balancers
* Single container setup bundles FalkorDB
* Streaming HTTP transport (STDIO still available for desktop)
**Testing** 4,000+ lines of test coverage across providers, async operations, and multi-database scenarios.
Breaking changes mostly around config migration from env vars to YAML. Full migration guide in docs.
Huge thanks to contributors, both individuals and from AWS, Microsoft, FalkorDB, Neo4j teams for drivers, reviews, and guidance.
Repo: [https://github.com/getzep/graphiti](https://github.com/getzep/graphiti)
--- TOP COMMENTS ---
Incredible achievement in such short time, well-deserved & thank you for the shoutout! (Dan from FalkorDB)
---
[deleted]..
12h ago
Tools
call-center-ai
Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!
15h ago
Applications
Realtime video analysis with Moondream
Live demo (no login required): [https://moondream.ai/solutions/analyze-live-video](https://moondream.ai/solutions/analyze-live-video)
Code: [https://github.com/m87-labs/Analyze-Live-Video-Solution](https://github.com/m87-labs/Analyze-Live-Video-Solution)
--- TOP COMMENTS ---
Love the transparent text overlay on the video feed for this purpose...
Code: [https://github.com/m87-labs/Analyze-Live-Video-Solution](https://github.com/m87-labs/Analyze-Live-Video-Solution)
--- TOP COMMENTS ---
Love the transparent text overlay on the video feed for this purpose...
Yesterday
Unipol Assicurazioni Leverages IBM watsonx to Build a New AI-driven Automation Platform
Tl;dr: Unipol Assicurazioni and IBM partnered to create an AI-driven automation platform (NAMI) leveraging watsonx for enhanced operational efficiency and AI capabilities.
Key Takeaways:
- Unipol Assicurazioni developed an AI-driven automation platform (NAMI) powered by IBM watsonx, reducing incident handling time by 90%.
- The platform utilizes IBM's next-generation mainframe and storage technologies alongside hybrid multi-cloud platforms like Red Hat OpenShift.
- Unipol's IT division designed NAMI to modernize IT operations within the company's on-premises infrastructure, blending security and scalability.
- NAMI's AI capabilities, built on watsonx, enable agile development, high performance, and strict control over sensitive data within the data centers.
17h ago
Research
A historians account of testing Gemini 3's (via A/B) ability to parse old English hand written documents on their benchmark, where they note that this model seems to excel not just at visual understanding, but symbolic reasoning, a great read - here are some snippets
https://generativehistory.substack.com/p/has-google-quietly-solved-two-of
--- TOP COMMENTS ---
Man I do hope Gemini 3.0 is a next step when it comes to translations. The last step we had was GPT 4.0. It has, not only stagnated, but in many cases gotten worse, as the AI companies started focusing more and more on coding.
---
As an amateur genealogist who is not so technologically skilled as to use tools like Transkribus with great expertise and results, this has long been a cherished dream of mine. I believe it will mark a breakthrough in historical research, even for professionals, since the paleographic reading and transcription of certain documents—such as royal chancery records prior to the sixteenth century, among many others—is no easy task. We are likely to witness significant discoveries, as there is a vast amount of documentation that has “slept” for centuries, still waiting to be read by researchers in historical archives...
--- TOP COMMENTS ---
Man I do hope Gemini 3.0 is a next step when it comes to translations. The last step we had was GPT 4.0. It has, not only stagnated, but in many cases gotten worse, as the AI companies started focusing more and more on coding.
---
As an amateur genealogist who is not so technologically skilled as to use tools like Transkribus with great expertise and results, this has long been a cherished dream of mine. I believe it will mark a breakthrough in historical research, even for professionals, since the paleographic reading and transcription of certain documents—such as royal chancery records prior to the sixteenth century, among many others—is no easy task. We are likely to witness significant discoveries, as there is a vast amount of documentation that has “slept” for centuries, still waiting to be read by researchers in historical archives...
9h ago
Prompt drift isn’t randomness — it’s structure decay
Run 1: “Perfect.”
Run 3: “Hmm, feels softer?”
Run 7: “Why does it sound polite again?”
You didn’t change the words. You didn’t reset the model. Yet something quietly shifted.
That’s not randomness — it’s structure decay. Each layer of the prompt slowly starts blurring into the next.
When tone, logic, and behavior all live in the same block, the model begins averaging them out. Over time, logic fades, tone resets, and the structure quietly collapses. That’s why single-block prompts never stay stable.
Tomorrow I’ll share how separating tone, logic, and behavior keeps your prompt alive past Run 7.
Have you noticed this quiet collapse before, or did it catch you off guard?
--- TOP COMMENTS ---
Do your fingers ever get tired from copy pasting chatGPT ourputs?
---
Hey fam I'm going to need you to do some research into model temperature..
Run 3: “Hmm, feels softer?”
Run 7: “Why does it sound polite again?”
You didn’t change the words. You didn’t reset the model. Yet something quietly shifted.
That’s not randomness — it’s structure decay. Each layer of the prompt slowly starts blurring into the next.
When tone, logic, and behavior all live in the same block, the model begins averaging them out. Over time, logic fades, tone resets, and the structure quietly collapses. That’s why single-block prompts never stay stable.
Tomorrow I’ll share how separating tone, logic, and behavior keeps your prompt alive past Run 7.
Have you noticed this quiet collapse before, or did it catch you off guard?
--- TOP COMMENTS ---
Do your fingers ever get tired from copy pasting chatGPT ourputs?
---
Hey fam I'm going to need you to do some research into model temperature..
22h ago
Google Deepmind: Robot Learning from a Physical World Model. Video model produces high quality robotics training data
[https://arxiv.org/abs/2511.07416](https://arxiv.org/abs/2511.07416)
--- TOP COMMENTS ---
https://preview.redd.it/sr09bnjfqk0g1.png?width=724&format=png&auto=webp&s=c17150d3692280ce61f9b7d5bc2ea7f99e7ad84e
Average task success **82%** vs **67%** for the strongest prior that imitates generated videos without a world model.
**Better transfer than hand-centric imitation:** object-centric policies vastly outperform embodiment-centric ones (e.g., *book→bookshelf* **90% vs 30%**; *shoe→shoebox* **80% vs 10%**).
scales as video models improve
---
This actually seems like a really clever training method..
--- TOP COMMENTS ---
https://preview.redd.it/sr09bnjfqk0g1.png?width=724&format=png&auto=webp&s=c17150d3692280ce61f9b7d5bc2ea7f99e7ad84e
Average task success **82%** vs **67%** for the strongest prior that imitates generated videos without a world model.
**Better transfer than hand-centric imitation:** object-centric policies vastly outperform embodiment-centric ones (e.g., *book→bookshelf* **90% vs 30%**; *shoe→shoebox* **80% vs 10%**).
scales as video models improve
---
This actually seems like a really clever training method..
20h ago
Full Replication of Google's Nested Learning Paper in PyTorch – code now live
Some of you may have seen Google Research’s [**Nested Learning paper**](https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/). They introduced HOPE, a self-modifying TITAN variant with a Continuum Memory System (multi-frequency FFN chain) + deep optimizer stack. They published the research but no code (like always), so I rebuilt the architecture and infra in PyTorch over the weekend.
Repo: https://github.com/kmccleary3301/nested_learning
## Highlights
- Level clock + CMS implementation (update-period gating, associative-memory optimizers).
- HOPE block w/ attention, TITAN memory, self-modifier pathway.
- Hydra configs for pilot/mid/target scales, uv-managed env, Deepspeed/FSDP launchers.
- Data pipeline: filtered RefinedWeb + supplements (C4, RedPajama, code) with tokenizer/sharding scripts.
- Evaluation: zero-shot harness covering PIQA, HellaSwag, WinoGrande, ARC-E/C, BoolQ, SIQA, CommonsenseQA, OpenBookQA + NIAH long-context script.
## What I need help with:
1. Running larger training configs (760M+, 4–8k context) and reporting W&B benchmarks.
2. Stress-testing CMS/self-modifier stability + alternative attention backbones.
3. Continual-learning evaluation (streaming domains) & regression tests.
If you try it, please file issues/PRs—especially around stability tricks, data pipelines, or eval scripts. Would love to see how it stacks up against these Qwen, DeepSeek, Minimax, and Kimi architectures.
--- TOP COMMENTS ---
Amazing!
---
Have you run some training/inference already? Did you manage to get the same numbers as in their report? I'm a bit confused, see some NotImplementted parts around https://github.com/kmccleary3301/nested_learning/blob/main/src/nested_learning/assoc_memory.py
How much of it is written by LLMs?..
Repo: https://github.com/kmccleary3301/nested_learning
## Highlights
- Level clock + CMS implementation (update-period gating, associative-memory optimizers).
- HOPE block w/ attention, TITAN memory, self-modifier pathway.
- Hydra configs for pilot/mid/target scales, uv-managed env, Deepspeed/FSDP launchers.
- Data pipeline: filtered RefinedWeb + supplements (C4, RedPajama, code) with tokenizer/sharding scripts.
- Evaluation: zero-shot harness covering PIQA, HellaSwag, WinoGrande, ARC-E/C, BoolQ, SIQA, CommonsenseQA, OpenBookQA + NIAH long-context script.
## What I need help with:
1. Running larger training configs (760M+, 4–8k context) and reporting W&B benchmarks.
2. Stress-testing CMS/self-modifier stability + alternative attention backbones.
3. Continual-learning evaluation (streaming domains) & regression tests.
If you try it, please file issues/PRs—especially around stability tricks, data pipelines, or eval scripts. Would love to see how it stacks up against these Qwen, DeepSeek, Minimax, and Kimi architectures.
--- TOP COMMENTS ---
Amazing!
---
Have you run some training/inference already? Did you manage to get the same numbers as in their report? I'm a bit confused, see some NotImplementted parts around https://github.com/kmccleary3301/nested_learning/blob/main/src/nested_learning/assoc_memory.py
How much of it is written by LLMs?..
Yesterday
Opinion And Analysis
It's been a big week for AI ; Here are 10 massive developments you might've missed:
* ChatGPT launches query interruption
* Gemini can read your Gmail and Drive
* Google’s Opal expands to 160+ countries
A collection of AI Updates!🧵
**1. China Bans Foreign AI Chips in State Data Centers**
Government requires new state-funded data center projects to only use domestically-made AI chips. Applies to all projects with any state funding.
This could be the start of a global chip conflict.
**2. ChatGPT Now Lets You Interrupt Queries**
Can now interrupt long-running queries and add new context without restarting or losing progress. Especially useful for refining deep research or GPT-5 Pro queries.
Real-time prompt adjustment will save lots of time.
**3. Gemini Deep Research Gets Gmail and Drive Access**
Available for all desktop users now, mobile soon. Combines live web research with internal documents for market analysis and competitor reports.
Deep research meets private data.
**4. Snapchat Makes Perplexity the Default AI for All Users**
Starting January, Perplexity becomes the default AI for all Snapchat users.
Deal begins in 2026 at $400M annually.
Capturing the younger demographic and early users through Snapchat.
**5. Google Labs Expands Opal to 160+ Countries**
No-code AI app builder grows from 15 to 160+ countries. Users create mini-apps with natural language for tasks like research automation and marketing campaigns.
Vibecoding apps is going global.
**6. OpenAI Launches GPT-5-Codex-Mini**
More compact, cost-efficient version allows 4x more usage. Plus, Business, and Edu get 50% higher rate limits. Pro and Enterprise get priority processing.
Have you tried this GPT-5-Codex Mini?
**7. Gamma Raises Series B at $2.1B Valuation**
AI presentation platform hits $100M ARR with just 50 employees ($2M per employee). 70M users creating 30M presentations monthly. API now public.
Genuinely disrupting PowerPoint.
**8. Circle Releases AI Coding Tools**
AI chatbot and MCP server generate code for integrating USDC, CCTP, Gateway, Wallets, and Contracts. Works in browser or IDEs like Cursor.
From idea to production faster.
**9. xAI is Hosting a Hackathon with Early Grok Model Access**
24-hour event with exclusive access to upcoming Grok models and X APIs. Applications open until November 22.
Early access to next-gen Grok models.
**10. Lovable Partners with Imagi to Bring Vibecoding to Schools**
Teachers can now use Lovable in classrooms - the same tool Fortune 500 companies use to build product lines.
OpenAI is making this possible.
**That's a wrap on this week's AI news.**
Which update surprised you most?
LMK if this was helpful | If so, I'll be posting more weekly AI + Agentic content!
--- TOP COMMENTS ---
Great summary. Thank you.
---
Good bot..
* Gemini can read your Gmail and Drive
* Google’s Opal expands to 160+ countries
A collection of AI Updates!🧵
**1. China Bans Foreign AI Chips in State Data Centers**
Government requires new state-funded data center projects to only use domestically-made AI chips. Applies to all projects with any state funding.
This could be the start of a global chip conflict.
**2. ChatGPT Now Lets You Interrupt Queries**
Can now interrupt long-running queries and add new context without restarting or losing progress. Especially useful for refining deep research or GPT-5 Pro queries.
Real-time prompt adjustment will save lots of time.
**3. Gemini Deep Research Gets Gmail and Drive Access**
Available for all desktop users now, mobile soon. Combines live web research with internal documents for market analysis and competitor reports.
Deep research meets private data.
**4. Snapchat Makes Perplexity the Default AI for All Users**
Starting January, Perplexity becomes the default AI for all Snapchat users.
Deal begins in 2026 at $400M annually.
Capturing the younger demographic and early users through Snapchat.
**5. Google Labs Expands Opal to 160+ Countries**
No-code AI app builder grows from 15 to 160+ countries. Users create mini-apps with natural language for tasks like research automation and marketing campaigns.
Vibecoding apps is going global.
**6. OpenAI Launches GPT-5-Codex-Mini**
More compact, cost-efficient version allows 4x more usage. Plus, Business, and Edu get 50% higher rate limits. Pro and Enterprise get priority processing.
Have you tried this GPT-5-Codex Mini?
**7. Gamma Raises Series B at $2.1B Valuation**
AI presentation platform hits $100M ARR with just 50 employees ($2M per employee). 70M users creating 30M presentations monthly. API now public.
Genuinely disrupting PowerPoint.
**8. Circle Releases AI Coding Tools**
AI chatbot and MCP server generate code for integrating USDC, CCTP, Gateway, Wallets, and Contracts. Works in browser or IDEs like Cursor.
From idea to production faster.
**9. xAI is Hosting a Hackathon with Early Grok Model Access**
24-hour event with exclusive access to upcoming Grok models and X APIs. Applications open until November 22.
Early access to next-gen Grok models.
**10. Lovable Partners with Imagi to Bring Vibecoding to Schools**
Teachers can now use Lovable in classrooms - the same tool Fortune 500 companies use to build product lines.
OpenAI is making this possible.
**That's a wrap on this week's AI news.**
Which update surprised you most?
LMK if this was helpful | If so, I'll be posting more weekly AI + Agentic content!
--- TOP COMMENTS ---
Great summary. Thank you.
---
Good bot..
9h ago
Products
Google Photos adds new AI features for editing, expands AI-powered search to over 100 countries
Google Photos has rolled out a series of new AI-powered features, including a way to edit objects and people in images, a new Ask button for AI-powered answers about the photo or edit requests, AI templates to create new photos, and the expansion of natural language search.
The company first introduced prompt-based editing for Pixel 10 series phones in August. Now, iOS users in the U.S. can describe their edits using voice or text to modify images, Google announced Tuesday. The company is also bringing its redesigned photo editor with easy editing options to iOS.
The upgrade includes a new personalized edits feature that recognizes people from Google Photos’ face groups. Users can find the “Help me edit” option and type several instructions applicable to people within the photo. For instance, “Remove Riley’s sunglasses, open my eyes, make Engel smile, and open her eyes” to get edits related to each person, as the image below shows.
The company is adding its popular AI image model, Nano Banana, to Google Photos to let users edit their photos to recreate images in new styles like a Renaissance portrait or cartoon strip.
Google is also adding AI templates to easily let users convert one photo into a specific format. With Nano Banana, formats like turning images into retro portraits or action figures became popular. The company said this feature will roll out next week on Android under the Create tab in the U.S. and India, where Nano Banana is used the most.
Google Photos is also getting a slight design change with a new Ask button that will act as a starting point for different AI requests. Users can ask for information about the photo, discover related moments, and edit it using prompts. The company will also show some suggestion chips to indicate what users can do with this feature, which is rolling out to users in the U.S. on iOS and Android.
Last year, Google introduced an AI-powered search feature for the Photos app with an initial launch in the United States. Today, the company is expanding the search to more than 100 countries, including Argentina, Australia, Brazil, Chile, Colombia, India, Indonesia, Japan, Mexico, New Zealand, Saudi Arabia, Singapore, and South Africa. It will support more than 17 new languages such as Arabic, Bengali, French, German, Hindi, Indonesian, Italian, Japanese, Portugese, and Spanish.
Join the Disrupt 2026 Waitlist
Add yourself to the Disrupt 2026 waitlist to be first in line when Early Bird tickets drop. Past Disrupts have brought Google Cloud, Netflix, Microsoft, Box, Phia, a16z, ElevenLabs, Wayve, Hugging Face, Elad Gil, and Vinod Khosla to the stages — part of 250+ industry leaders driving 200+ sessions built to fuel your growth and sharpen your edge. Plus, meet the hundreds of startups innovating across every sector.
Join the Disrupt 2026 Waitlist
Add yourself to the Disrupt 2026 waitlist to be first in line when Early Bird tickets drop. Past Disrupts have brought Google Cloud, Netflix, Microsoft, Box, Phia, a16z, ElevenLabs, Wayve, Hugging Face, Elad Gil, and Vinod Khosla to the stages — part of 250+ industry leaders driving 200+ sessions built to fuel your growth and sharpen your edge. Plus, meet the hundreds of startups innovating across every sector...
The company first introduced prompt-based editing for Pixel 10 series phones in August. Now, iOS users in the U.S. can describe their edits using voice or text to modify images, Google announced Tuesday. The company is also bringing its redesigned photo editor with easy editing options to iOS.
The upgrade includes a new personalized edits feature that recognizes people from Google Photos’ face groups. Users can find the “Help me edit” option and type several instructions applicable to people within the photo. For instance, “Remove Riley’s sunglasses, open my eyes, make Engel smile, and open her eyes” to get edits related to each person, as the image below shows.
The company is adding its popular AI image model, Nano Banana, to Google Photos to let users edit their photos to recreate images in new styles like a Renaissance portrait or cartoon strip.
Google is also adding AI templates to easily let users convert one photo into a specific format. With Nano Banana, formats like turning images into retro portraits or action figures became popular. The company said this feature will roll out next week on Android under the Create tab in the U.S. and India, where Nano Banana is used the most.
Google Photos is also getting a slight design change with a new Ask button that will act as a starting point for different AI requests. Users can ask for information about the photo, discover related moments, and edit it using prompts. The company will also show some suggestion chips to indicate what users can do with this feature, which is rolling out to users in the U.S. on iOS and Android.
Last year, Google introduced an AI-powered search feature for the Photos app with an initial launch in the United States. Today, the company is expanding the search to more than 100 countries, including Argentina, Australia, Brazil, Chile, Colombia, India, Indonesia, Japan, Mexico, New Zealand, Saudi Arabia, Singapore, and South Africa. It will support more than 17 new languages such as Arabic, Bengali, French, German, Hindi, Indonesian, Italian, Japanese, Portugese, and Spanish.
Join the Disrupt 2026 Waitlist
Add yourself to the Disrupt 2026 waitlist to be first in line when Early Bird tickets drop. Past Disrupts have brought Google Cloud, Netflix, Microsoft, Box, Phia, a16z, ElevenLabs, Wayve, Hugging Face, Elad Gil, and Vinod Khosla to the stages — part of 250+ industry leaders driving 200+ sessions built to fuel your growth and sharpen your edge. Plus, meet the hundreds of startups innovating across every sector.
Join the Disrupt 2026 Waitlist
Add yourself to the Disrupt 2026 waitlist to be first in line when Early Bird tickets drop. Past Disrupts have brought Google Cloud, Netflix, Microsoft, Box, Phia, a16z, ElevenLabs, Wayve, Hugging Face, Elad Gil, and Vinod Khosla to the stages — part of 250+ industry leaders driving 200+ sessions built to fuel your growth and sharpen your edge. Plus, meet the hundreds of startups innovating across every sector...
11h ago
ElevenLabs’ new AI marketplace lets brands use famous voices for ads
Tl;dr: ElevenLabs introduces an online marketplace where brands can license AI-replicated voices of famous figures for advertisements and content, resolving ethical concerns around AI-generated celebrity voices.
Key Takeaways:
- ElevenLabs' new Iconic Voice Marketplace offers AI-generated voices of famous figures like Michael Caine, Liza Minelli, and Mark Twain.
- The platform connects companies with voice owners, providing a consent-based approach to utilizing AI-generated celebrity voices.
- ElevenLabs claims to ensure that the voices are generated with permission, transparency, and fair compensation for the owners.
- The marketplace is open to a curated list of verified, iconic talent and IP owners.
11h ago
Your Google Photos just got 3 huge Nano Banana image editing upgrades - for free
Tl;dr: Google Photos has released several AI-powered features for photo editing, including targeted edits, personalized templates, and a new Ask button, all leveraging Nano Banana technology.
Key Takeaways:
- Google Photos now includes targeted edits using simple text or voice prompts, powered by Nano Banana, allowing users to make specific changes to their images.
- The 'Create with AI' feature, available on Android, offers themed templates for instant edits without writing a prompt, with personalized templates coming soon.
- A new Ask button lets users request edits or ask more about an image's content, including finding similar photos or learning about the image itself.
- These AI tools are rolling out to the Google Photos app on Android and iOS, with the latest version required for access.
11h ago
Google Photos lets iPhone users edit images by describing changes
Tl;dr: Google Photos is expanding its AI-powered editing features with iOS support, including conversational edits and improved facial recognition. The update also introduces a new AI model and templates for Android users.
Key Takeaways:
- Google Photos is releasing iOS support for its 'Help me edit' feature, allowing users to describe changes they want made to their photos using voice or text.
- The update also enhances facial editing capabilities by referencing images from users' private face groups.
- Google's Nano Banana AI model is being integrated into Google Photos, enabling users to apply styles like paintings and mosaics to their images.
- The 'Ask' button is being added to the Android and iOS versions of Google Photos, providing a chatbot-style interface for editing images and answering questions.
11h ago
6 new things you can do with AI in Google Photos
Tl;dr: Google Photos has added six new AI-powered features, including personalized edits, image restyling with Nano Banana, and expanded search capabilities.
Key Takeaways:
- Google Photos now allows users to edit photos by asking the app to make specific changes, such as removing sunglasses or fixing a smile.
- The app has added Nano Banana, Gemini's top-rated image editing model, which enables users to restyle their images in new ways, such as painting themselves as a Renaissance portrait.
- Google Photos has also expanded its search capabilities with Ask Photos, which now supports 17 new languages and will be available in more than 100 countries and regions.
- The app will soon introduce personalized templates that use insights from users' photo galleries to create unique edits based on their hobbies and experiences.
- Google is rolling out these features to users in the US, India, and other countries, with more countries and regions to follow.
11h ago
Microsoft just expanded their AI certification track again!
Microsoft just announced 3 new AI-related certifications, right after releasing AB-100 in beta last month.
**New exams:**
* **AB-900: Copilot & Agent Administration Fundamentals**
* **AB-730: AI Business Professional**
* **AB-731: AI Transformation Leader**
This looks like Microsoft is building a full **business + enablement track** for AI, not just technical Azure AI engineer paths.
The new certs seem to target:
* Business and project leads
* Teams deploying Copilot in organizations
* People involved in AI strategy and process modernization
So instead of model-building or ML pipelines, these focus more on:
* AI governance
* AI adoption planning
* Business transformation with AI tools
Is anyone here planning to take these? And has anyone tried AB-100 yet?
--- TOP COMMENTS ---
They seem quite relevant, how is the enrollement/costs?
---
The absurdity of not getting to use AI on exams about using AI is mind boggling. if the AI is not there the job itself is not there...
**New exams:**
* **AB-900: Copilot & Agent Administration Fundamentals**
* **AB-730: AI Business Professional**
* **AB-731: AI Transformation Leader**
This looks like Microsoft is building a full **business + enablement track** for AI, not just technical Azure AI engineer paths.
The new certs seem to target:
* Business and project leads
* Teams deploying Copilot in organizations
* People involved in AI strategy and process modernization
So instead of model-building or ML pipelines, these focus more on:
* AI governance
* AI adoption planning
* Business transformation with AI tools
Is anyone here planning to take these? And has anyone tried AB-100 yet?
--- TOP COMMENTS ---
They seem quite relevant, how is the enrollement/costs?
---
The absurdity of not getting to use AI on exams about using AI is mind boggling. if the AI is not there the job itself is not there...
18h ago
Funding
SoftBank Sells $5.8 Billion Stake in Nvidia to Pay for OpenAI Deals - The New York Times
Tl;dr: SoftBank sold its entire $5.8 billion holding in Nvidia to fund OpenAI investments, sparking concerns about the A.I. stock market
Key Takeaways:
- SoftBank sold its $5.8 billion stake in Nvidia to fund OpenAI investments.
- The move has raised concerns among investors that the A.I. stock market rally may be overdone.
- SoftBank needs to divest existing portfolio to finance its A.I. focus, according to its CFO Yoshimitsu Goto.
11h ago
Wonderful raised $100M Series A to put AI agents on the front lines of customer service
Tl;dr: Wonderful, an Israeli AI agent startup, raised $100 million in a Series A round led by Index Ventures to expand its customer-facing AI agent platform globally.
Key Takeaways:
- Wonderful, the Israeli AI agent startup, secured $100 million in a Series A funding round led by Index Ventures.
- The startup aims to launch in Germany, Austria, the Nordics, and Portugal in 2025, and the Asia-Pacific region in 2026.
- Wonderful's AI agents are currently managing tens of thousands of customer requests daily with an 80% resolve rate.
11h ago
Companies
AMD CEO says AI market is 'faster than anything we've seen before,' sees $1T by 2030 - Seeking Alpha
- Tech
- Top News
AMD CEO says AI market is 'faster than anything we've seen before,' sees $1T by 2030
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JHVEPhoto
AMD (AMD) made several updates at its financial analyst day on Tuesday, including reiterating the theme that artificial intelligence spending is not likely to slow down anytime soon.
Speaking at the company's first analyst day since 2022, AMD CEO and Chairman
Quick Insights
- What are AMD's growth prospects in the AI data center market?AMD predicts the AI data center market could reach $1T by 2030, aims for tens of billions in revenue in 2027, and targets doubling its share with an 80% compound annual growth rate over the next three to five years.
What are AMD's growth prospects in the AI data center market?
- How does AMD plan to increase its revenue and market share in the AI sector?AMD plans to leverage products like MI450 and Helios rack-scale, expand custom design wins in sectors such as aerospace, defense, and communications, and ramp up revenue based on major design wins set to activate from 2026 onward.
How does AMD plan to increase its revenue and market share in the AI sector?
- What competitive advantages does AMD cite amid rising AI infrastructure investment?AMD emphasizes strong balance sheets, increasing demand for AI compute solutions, regular product innovation cycles, and expansion into major AI companies through Instinct GPUs and data center CPUs commanding significant revenue share.
What competitive advantages does AMD cite amid rising AI infrastructure investment?
Recommended For You..
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AMD CEO says AI market is 'faster than anything we've seen before,' sees $1T by 2030
- 5Share
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JHVEPhoto
AMD (AMD) made several updates at its financial analyst day on Tuesday, including reiterating the theme that artificial intelligence spending is not likely to slow down anytime soon.
Speaking at the company's first analyst day since 2022, AMD CEO and Chairman
Quick Insights
- What are AMD's growth prospects in the AI data center market?AMD predicts the AI data center market could reach $1T by 2030, aims for tens of billions in revenue in 2027, and targets doubling its share with an 80% compound annual growth rate over the next three to five years.
What are AMD's growth prospects in the AI data center market?
- How does AMD plan to increase its revenue and market share in the AI sector?AMD plans to leverage products like MI450 and Helios rack-scale, expand custom design wins in sectors such as aerospace, defense, and communications, and ramp up revenue based on major design wins set to activate from 2026 onward.
How does AMD plan to increase its revenue and market share in the AI sector?
- What competitive advantages does AMD cite amid rising AI infrastructure investment?AMD emphasizes strong balance sheets, increasing demand for AI compute solutions, regular product innovation cycles, and expansion into major AI companies through Instinct GPUs and data center CPUs commanding significant revenue share.
What competitive advantages does AMD cite amid rising AI infrastructure investment?
Recommended For You..
6h ago
Meta’s chief AI scientist Yann LeCun reportedly plans to leave to build his own startup
Tl;dr: Meta's top AI scientist Yann LeCun plans to leave the company to start his own AI-focused startup, focusing on world models, amidst a period of significant organizational change at Meta.
Key Takeaways:
- Yann LeCun, Meta's chief AI scientist and A.M. Turing Award winner, plans to leave the company to build a startup focusing on world models.
- LeCun's departure comes amid Meta's AI organization overhaul, driven by concerns it is being outpaced by rivals in AI research and development.
- Meta has invested heavily in AI talent and infrastructure, including hiring over 50 engineers and researchers, but LeCun's long-term research work is expected to be affected by the changes.
- LeCun has been vocal about his concerns regarding the current state of AI technology and its overmarketing as a solution to global problems.
13h ago
OpenAI hires Intel's CTO and AI lead
Wonder if it is for the design of their custom chips.
Original tweet: [https://x.com/gdb/status/1987996461846659372?s=20](https://x.com/gdb/status/1987996461846659372?s=20)
--- TOP COMMENTS ---
partly, i am sure. hardware build out....
Original tweet: [https://x.com/gdb/status/1987996461846659372?s=20](https://x.com/gdb/status/1987996461846659372?s=20)
--- TOP COMMENTS ---
partly, i am sure. hardware build out....
Yesterday
Developer Tools
I improved the recently-showcased Claude Skills reference architecture to (1) analyze prompt intent and rank skills accordingly – using Haiku – and (2) auto-inject skills for friction-free, highly deterministic skill-loading workflows. Repo and more details are in the description.
Hello, fellow Clauders!
-----
**TLDR**: Refined a popular reference architecture that was recently shared to drastically boost relevant skill selection and replace suggestion patterns with injection patterns to eliminate any and all friction.
**[Repo is here](https://github.com/jefflester/claude-skills-supercharged).**
-----
I've been working my ass off for the last few months on a project that has continuously grown in scale (it's an MCP server that exposes all traditional UI-driven debugging operations through an API, currently supporting Python, Java, and JavaScript/TypeScript).
As the codebase expanded, it became increasingly difficult to work with efficiency: wasting my time, again and again, explaining concepts; relying on custom commands (that I had to remember to run) to avoid so much copy/paste; and so on.
Then I saw u/JokeGold5455's post: [Claude Code is a Beast – Tips from 6 Months of Hardcore Use](https://www.reddit.com/r/ClaudeAI/comments/1oivjvm/claude_code_is_a_beast_tips_from_6_months_of/). It sounded amazing, so I spent a few days integrating it with my repository.
I ran into some issues while working with the system, though.
- Skills were not reliably injected; rather, they were recommended.
- Skills were not reliably selected – intent patterns/keywords often would select skills that were not the best skill for the given prompt.
So, I...
- Removed keyword/intent patterns, and replaced them with a Haiku prompt analyzer. This hook runs *before* the main agent even sees your prompt.
- Replaced suggestions with injections. Once a skill(s) are identified as critical via the Haiku prompt analyzer, the prompts are injected into the main agent thread.
This means: highly accurate skill selection and a frictionless workflow for both myself and the agent. The system is absolutely amazing now – for me, at least.
*Note*: the one downside to this implementation is that it does have additional cost, albeit very cheap. I've sent a couple thousand prompts over the last 8 days, and my Haiku spend is up to about $1.20. For heavy users, I can't see monthly spend ever eclipsing like $8.
If you have questions, let me know. I'd be happy to answer them!
## Resources
- [My repo](https://github.com/jefflester/claude-skills-supercharged)
- [Original reference architecture](https://github.com/diet103/claude-code-infrastructure-showcase)
- [Original Reddit post](https://www.reddit.com/r/ClaudeAI/comments/1oivjvm/claude_code_is_a_beast_tips_from_6_months_of/)
--- TOP COMMENTS ---
HOLY COW!! Thank you Ben Affleck 🫡
---
This is a great idea. Thanks for sharing! I think I’ll try out Gemini for some different perspective. Probably worth testing out but definitely an improvement...
-----
**TLDR**: Refined a popular reference architecture that was recently shared to drastically boost relevant skill selection and replace suggestion patterns with injection patterns to eliminate any and all friction.
**[Repo is here](https://github.com/jefflester/claude-skills-supercharged).**
-----
I've been working my ass off for the last few months on a project that has continuously grown in scale (it's an MCP server that exposes all traditional UI-driven debugging operations through an API, currently supporting Python, Java, and JavaScript/TypeScript).
As the codebase expanded, it became increasingly difficult to work with efficiency: wasting my time, again and again, explaining concepts; relying on custom commands (that I had to remember to run) to avoid so much copy/paste; and so on.
Then I saw u/JokeGold5455's post: [Claude Code is a Beast – Tips from 6 Months of Hardcore Use](https://www.reddit.com/r/ClaudeAI/comments/1oivjvm/claude_code_is_a_beast_tips_from_6_months_of/). It sounded amazing, so I spent a few days integrating it with my repository.
I ran into some issues while working with the system, though.
- Skills were not reliably injected; rather, they were recommended.
- Skills were not reliably selected – intent patterns/keywords often would select skills that were not the best skill for the given prompt.
So, I...
- Removed keyword/intent patterns, and replaced them with a Haiku prompt analyzer. This hook runs *before* the main agent even sees your prompt.
- Replaced suggestions with injections. Once a skill(s) are identified as critical via the Haiku prompt analyzer, the prompts are injected into the main agent thread.
This means: highly accurate skill selection and a frictionless workflow for both myself and the agent. The system is absolutely amazing now – for me, at least.
*Note*: the one downside to this implementation is that it does have additional cost, albeit very cheap. I've sent a couple thousand prompts over the last 8 days, and my Haiku spend is up to about $1.20. For heavy users, I can't see monthly spend ever eclipsing like $8.
If you have questions, let me know. I'd be happy to answer them!
## Resources
- [My repo](https://github.com/jefflester/claude-skills-supercharged)
- [Original reference architecture](https://github.com/diet103/claude-code-infrastructure-showcase)
- [Original Reddit post](https://www.reddit.com/r/ClaudeAI/comments/1oivjvm/claude_code_is_a_beast_tips_from_6_months_of/)
--- TOP COMMENTS ---
HOLY COW!! Thank you Ben Affleck 🫡
---
This is a great idea. Thanks for sharing! I think I’ll try out Gemini for some different perspective. Probably worth testing out but definitely an improvement...
6h ago
Language Server Protocol (LSP), Skills (w/ Beastmode), and Agents/Subagents - where are we now
Change comes fast with Claude. I only recently caught up with the excellent post by u/JokeGold5485 about their [BEASTMODE](https://www.reddit.com/r/ClaudeAI/s/6YVQ3CnnM7) setup that automatically called Skills without fail.
Then I read about the hidden [LSP mode](https://www.reddit.com/r/ClaudeAI/s/KDSAR8PfCh) that is coming soon.
There’s also [Zen MCP](https://github.com/BeehiveInnovations/zen-mcp-server), which allows you to use Gemini and other LLMs natively inside Claude.
So my question is, what is the most up-to-date guide and setup to be able to save context, use skills effectively, integrate other LLMs, and automate my workflow?
Some mad scientist out there has got to have cracked the code!
--- TOP COMMENTS ---
yeah the landscape is moving fast. lsp mode + skills is gonna be powerful
for agent coordination i'm using https://github.com/mbruhler/claude-orchestration:
skill1 -> (agent1 || agent2) -> @review -> skill2 -> output
lets u mix skills w/ parallel agents and checkpoints. the workflow syntax makes it explicit vs beastmode's auto-calling. more control over when stuff executes
---
Someone please just create a autocomplete that rivals cursors…that’s my dream. Don’t say windsurf it’s trash.
If you are scraping comments for app ideas read this!!!..
12h ago
How exactly do coders use AI tools in their IDEs?
I do have a premium subscription to some LLMs. My process would be to just copy each file code from the directory or just send the code dump to the browser chat, and it would communicate with me accordingly. But since my projects now are getting large (5k+ loc), it's getting hard to maintain AI's context for all the code, and it hallucinates a lot. So exactly how do experienced people use these tools efficiently? Please elaborate.
--- TOP COMMENTS ---
Cursor, GitHub Copilot, Claude Code, Codex, Gemini CLI. Take your pick.
---
CLI..
--- TOP COMMENTS ---
Cursor, GitHub Copilot, Claude Code, Codex, Gemini CLI. Take your pick.
---
CLI..
22h ago
Open Source
gpt-oss-120b on Cerebras
gpt-oss-120b reasoning CoT on Cerebras be like
--- TOP COMMENTS ---
Is gpt-oss worse on Cerbras? I actually really like gpt-oss(granted I can't use many of the other models due to corporate requirements). It's a significant bump over llama 3.3 and llama 4.
---
Cerebras is running GLM 4.6 on API now. Looks to be 500 t/s decoding on average. And they tend to put speculative decoding that speeds up coding a lot too. I think it's a possible value add, has anyone tried it on real tasks so far?..
--- TOP COMMENTS ---
Is gpt-oss worse on Cerbras? I actually really like gpt-oss(granted I can't use many of the other models due to corporate requirements). It's a significant bump over llama 3.3 and llama 4.
---
Cerebras is running GLM 4.6 on API now. Looks to be 500 t/s decoding on average. And they tend to put speculative decoding that speeds up coding a lot too. I think it's a possible value add, has anyone tried it on real tasks so far?..
9h ago
Agentic RAG: from Zero to Hero
Hi everyone,
After spending several months building agents and experimenting with RAG systems, I decided to publish a GitHub repository to help those who are approaching agents and RAG for the first time.
I created an **agentic RAG** with an educational purpose, aiming to provide a clear and practical reference. When I started, I struggled to find a single, structured place where all the key concepts were explained. I had to gather information from many different sources—and that’s exactly why I wanted to build something more accessible and beginner-friendly.
---
## 📚 What you’ll learn in this repository
An end-to-end walkthrough of the essential building blocks:
- **PDF → Markdown conversion**
- **Hierarchical chunking** (parent/child structure)
- **Hybrid embeddings** (dense + sparse)
- **Vector storage** of chunks using *Qdrant*
- **Parallel multi-query handling** — ability to generate and evaluate multiple queries simultaneously
- **Query rewriting** — automatically rephrases unclear or incomplete queries before retrieval
- **Human-in-the-loop** to clarify ambiguous user queries
- **Context management** across multiple messages using summarization
- A **fully working agentic RAG** using LangGraph that retrieves, evaluates, corrects, and generates answers
- **Simple chatbot** using Gradio library
---
I hope this repository can be helpful to anyone starting their journey.
Thanks to everyone who takes a look and finds it useful! GitHub: https://github.com/GiovanniPasq/agentic-rag-for-dummies
--- TOP COMMENTS ---
Thanks man I’ll check this out. My local RAG always sucks, it’ll be good seeing how I can get better. I get the data, it’s organized, embedded, into the VDB, I smack an agent and front end on there, and it hallucinates like Willie Nelson at a Folk Music Festival or fails to know what it knows when it needs to know it. Thank you so much for sharing.
---
*Why dance with a hero when you can dance with a zero?*..
After spending several months building agents and experimenting with RAG systems, I decided to publish a GitHub repository to help those who are approaching agents and RAG for the first time.
I created an **agentic RAG** with an educational purpose, aiming to provide a clear and practical reference. When I started, I struggled to find a single, structured place where all the key concepts were explained. I had to gather information from many different sources—and that’s exactly why I wanted to build something more accessible and beginner-friendly.
---
## 📚 What you’ll learn in this repository
An end-to-end walkthrough of the essential building blocks:
- **PDF → Markdown conversion**
- **Hierarchical chunking** (parent/child structure)
- **Hybrid embeddings** (dense + sparse)
- **Vector storage** of chunks using *Qdrant*
- **Parallel multi-query handling** — ability to generate and evaluate multiple queries simultaneously
- **Query rewriting** — automatically rephrases unclear or incomplete queries before retrieval
- **Human-in-the-loop** to clarify ambiguous user queries
- **Context management** across multiple messages using summarization
- A **fully working agentic RAG** using LangGraph that retrieves, evaluates, corrects, and generates answers
- **Simple chatbot** using Gradio library
---
I hope this repository can be helpful to anyone starting their journey.
Thanks to everyone who takes a look and finds it useful! GitHub: https://github.com/GiovanniPasq/agentic-rag-for-dummies
--- TOP COMMENTS ---
Thanks man I’ll check this out. My local RAG always sucks, it’ll be good seeing how I can get better. I get the data, it’s organized, embedded, into the VDB, I smack an agent and front end on there, and it hallucinates like Willie Nelson at a Folk Music Festival or fails to know what it knows when it needs to know it. Thank you so much for sharing.
---
*Why dance with a hero when you can dance with a zero?*..
10h ago
Half-trillion parameter model on a machine with 128 GB RAM + 24 GB VRAM
Hi everyone,
just wanted to share that I’ve successfully run **Qwen3-Coder-480B** on **llama.cpp** using the following setup:
* **CPU:** Intel i9-13900KS
* **RAM:** 128 GB (DDR5 4800 MT/s)
* **GPU:** RTX 4090 (24 GB VRAM)
I’m using the **4-bit and 3-bit Unsloth quantizations** from Hugging Face: [https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF)
**Performance results:**
* **UD-Q3\_K\_XL:** \~2.0 tokens/sec (generation)
* **UD-Q4\_K\_XL:** \~1.0 token/sec (generation)
**Command lines used (llama.cpp):**
`llama-server \`
`--threads 32 --jinja --flash-attn on \`
`--cache-type-k q8_0 --cache-type-v q8_0 \`
`--model/Qwen3-Coder-480B-A35B-Instruct-UD-Q3_K_XL-00001-of-00005.gguf \`
`--ctx-size 131072 --n-cpu-moe 9999 --no-warmup`
`llama-server \`
`--threads 32 --jinja --flash-attn on \`
`--cache-type-k q8_0 --cache-type-v q8_0 \`
`--model/Qwen3-Coder-480B-A35B-Instruct-UD-Q4_K_XL-00001-of-00006.gguf \`
`--ctx-size 131072 --n-cpu-moe 9999 --no-warmup`
**Important:** The *--no-warmup* flag is **required** \- without it, the process will terminate before you can start chatting.
In short: yes, it’s possible to run a **half-trillion parameter model** on a machine with **128 GB RAM + 24 GB VRAM**!
--- TOP COMMENTS ---
it's a crawl not run.
---
I think it's cool you tried...
just wanted to share that I’ve successfully run **Qwen3-Coder-480B** on **llama.cpp** using the following setup:
* **CPU:** Intel i9-13900KS
* **RAM:** 128 GB (DDR5 4800 MT/s)
* **GPU:** RTX 4090 (24 GB VRAM)
I’m using the **4-bit and 3-bit Unsloth quantizations** from Hugging Face: [https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF)
**Performance results:**
* **UD-Q3\_K\_XL:** \~2.0 tokens/sec (generation)
* **UD-Q4\_K\_XL:** \~1.0 token/sec (generation)
**Command lines used (llama.cpp):**
`llama-server \`
`--threads 32 --jinja --flash-attn on \`
`--cache-type-k q8_0 --cache-type-v q8_0 \`
`--model
`--ctx-size 131072 --n-cpu-moe 9999 --no-warmup`
`llama-server \`
`--threads 32 --jinja --flash-attn on \`
`--cache-type-k q8_0 --cache-type-v q8_0 \`
`--model
`--ctx-size 131072 --n-cpu-moe 9999 --no-warmup`
**Important:** The *--no-warmup* flag is **required** \- without it, the process will terminate before you can start chatting.
In short: yes, it’s possible to run a **half-trillion parameter model** on a machine with **128 GB RAM + 24 GB VRAM**!
--- TOP COMMENTS ---
it's a crawl not run.
---
I think it's cool you tried...
10h ago
ChatGPT lied to me so I built an AI Scientist.
100% open-source. With access to 100$ of PubMed, arXiv, bioRxiv, medRxiv, dailymed, and every clinical trial.
I was at a top london university watching biology phd students waste entire days because every single ai tool is fundamentally broken. These are smart people doing actual research. Comparing car-t efficacy across trials. Tracking adc adverse events. Trying to figure out why their $50,000 mouse model won't replicate results from a paper published six months ago.
They ask chatgpt about a 2024 pembrolizumab trial. It confidently cites a paper. The paper does not exist. It made it up. My friend asked three different ais for keynote-006 orr values. Three different numbers. All wrong. Not even close. Just completely fabricated.
This is actually insane. The information exists. Right now. 37 million papers on pubmed. Half a million registered trials. Every preprint ever posted. Every fda label. Every protocol amendment. All of it indexed. All of it public. All of it free. You can query it via api in 100 milliseconds.
But you ask an ai and it just fucking lies to you. Not because gpt-4 or claude are bad models- they're incredible at reasoning- they just literally cannot read anything. They're doing statistical parlor tricks on training data from 2023. They have no eyes. They are completely blind.
The databases exist. The apis exist. The models exist. Someone just needs to connect three things. This is not hard. This should not be a novel contribution!
So I built it. In a weekend.
**What it has access to:**
* **PubMed** (37M+ papers, full metadata + abstracts)
* **arXiv, bioRxiv, medRxiv** (every preprint in bio/physics/CS)
* **Clinical trials gov** (complete trial registry)
* **DailyMed** (FDA drug labels and safety data)
* **Live web search** (useful for realtime news/company research, etc)
It doesn't summarize based on training data. It reads the actual papers. Every query hits the primary literature and returns structured, citable results.
**Technical Capabilities:**
Prompt it: "Pembrolizumab vs nivolumab in NSCLC. Pull Phase 3 data, compute ORR deltas, plot survival curves, export tables."
Execution chain:
1. Query clinical trial registry + PubMed for matching studies
2. Retrieve full trial protocols and published results
3. Parse endpoints, patient demographics, efficacy data
4. Execute Python: statistical analysis, survival modeling, visualization
5. Generate report with citations, confidence intervals, and exportable datasets
What takes a research associate 40 hours happens in 3 minutes. With references.
**Tech Stack:**
Search Infrastructure:
* Valyu Search API (just this search API gives the agent access to all the biomedical data, pubmed/clinicaltrials/etc)
Execution:
* Daytona (sandboxed Python runtime)
* Vercel AI SDK (the best framework for agents + tool calling)
* Next.js + Supabase
* Can also hook up to local LLMs via Ollama / LMStudio
Fully open-source, self-hostable, and model-agnostic. I also built a hosted version so you can test it without setting anything up. If something's broken or missing pls let me know!
Leaving the repo in the comments!
--- TOP COMMENTS ---
It is fully open-source!
would love feedback: [Github repo](https://github.com/yorkeccak/bio)
---
does this API access data beyond bio too?..
I was at a top london university watching biology phd students waste entire days because every single ai tool is fundamentally broken. These are smart people doing actual research. Comparing car-t efficacy across trials. Tracking adc adverse events. Trying to figure out why their $50,000 mouse model won't replicate results from a paper published six months ago.
They ask chatgpt about a 2024 pembrolizumab trial. It confidently cites a paper. The paper does not exist. It made it up. My friend asked three different ais for keynote-006 orr values. Three different numbers. All wrong. Not even close. Just completely fabricated.
This is actually insane. The information exists. Right now. 37 million papers on pubmed. Half a million registered trials. Every preprint ever posted. Every fda label. Every protocol amendment. All of it indexed. All of it public. All of it free. You can query it via api in 100 milliseconds.
But you ask an ai and it just fucking lies to you. Not because gpt-4 or claude are bad models- they're incredible at reasoning- they just literally cannot read anything. They're doing statistical parlor tricks on training data from 2023. They have no eyes. They are completely blind.
The databases exist. The apis exist. The models exist. Someone just needs to connect three things. This is not hard. This should not be a novel contribution!
So I built it. In a weekend.
**What it has access to:**
* **PubMed** (37M+ papers, full metadata + abstracts)
* **arXiv, bioRxiv, medRxiv** (every preprint in bio/physics/CS)
* **Clinical trials gov** (complete trial registry)
* **DailyMed** (FDA drug labels and safety data)
* **Live web search** (useful for realtime news/company research, etc)
It doesn't summarize based on training data. It reads the actual papers. Every query hits the primary literature and returns structured, citable results.
**Technical Capabilities:**
Prompt it: "Pembrolizumab vs nivolumab in NSCLC. Pull Phase 3 data, compute ORR deltas, plot survival curves, export tables."
Execution chain:
1. Query clinical trial registry + PubMed for matching studies
2. Retrieve full trial protocols and published results
3. Parse endpoints, patient demographics, efficacy data
4. Execute Python: statistical analysis, survival modeling, visualization
5. Generate report with citations, confidence intervals, and exportable datasets
What takes a research associate 40 hours happens in 3 minutes. With references.
**Tech Stack:**
Search Infrastructure:
* Valyu Search API (just this search API gives the agent access to all the biomedical data, pubmed/clinicaltrials/etc)
Execution:
* Daytona (sandboxed Python runtime)
* Vercel AI SDK (the best framework for agents + tool calling)
* Next.js + Supabase
* Can also hook up to local LLMs via Ollama / LMStudio
Fully open-source, self-hostable, and model-agnostic. I also built a hosted version so you can test it without setting anything up. If something's broken or missing pls let me know!
Leaving the repo in the comments!
--- TOP COMMENTS ---
It is fully open-source!
would love feedback: [Github repo](https://github.com/yorkeccak/bio)
---
does this API access data beyond bio too?..
10h ago
Egocentric-10K is the largest egocentric dataset. It is the first dataset collected exclusively in real factories (Build AI - 10,000 hours - 2,153 factory workers - 1,080,000,000 frame)
Hugging Face, (apache 2.0): [https://huggingface.co/datasets/builddotai/Egocentric-10K](https://huggingface.co/datasets/builddotai/Egocentric-10K)
Eddy Xu on 𝕏: [https://x.com/eddybuild/status/1987951619804414416](https://x.com/eddybuild/status/1987951619804414416)
--- TOP COMMENTS ---
the middle one looks like weed nugs
---
Just so you all understand the context:
The humanoid robotics companies believe that data is the current limitation. They are buying and amassing large amounts of data to try and get their robots to solve factory and everyday tasks. Light levels of this look like people wearing POV cameras such as this. Heavier and more expensive versions involve tele-operated robot datasets, full body tracking suits + POV, and more.
Having an open-source version of this is NOT immoral, as it leads to the future where open models can be made more easily within the robotics space. This being open is great.
Now the only real issue I see is what the reasoning for this is. Is it a democratization of knowledge? Or is it flailing because results haven't been good enough yet for widespread adoption. I hope it's the first!..
Eddy Xu on 𝕏: [https://x.com/eddybuild/status/1987951619804414416](https://x.com/eddybuild/status/1987951619804414416)
--- TOP COMMENTS ---
the middle one looks like weed nugs
---
Just so you all understand the context:
The humanoid robotics companies believe that data is the current limitation. They are buying and amassing large amounts of data to try and get their robots to solve factory and everyday tasks. Light levels of this look like people wearing POV cameras such as this. Heavier and more expensive versions involve tele-operated robot datasets, full body tracking suits + POV, and more.
Having an open-source version of this is NOT immoral, as it leads to the future where open models can be made more easily within the robotics space. This being open is great.
Now the only real issue I see is what the reasoning for this is. Is it a democratization of knowledge? Or is it flailing because results haven't been good enough yet for widespread adoption. I hope it's the first!..
12h ago
Hi reddit, I rebuilt Karpathy's Nanochat in pure Rust [nanochat-rs]
The repo is at: [https://github.com/AntigmaLabs/nanochat-rs](https://github.com/AntigmaLabs/nanochat-rs)
The goal to provide the community with a reference implementation in a different language and possibly a clean nice little hackable cognitive core that is easier to understand and deploy(without the python weak types and heavy pytorch dependencies)
Main features
* Native rust
* Integration with HuggingFace
* Centralized model loader resilient to tensor name changes
* Minimal surface area to keep cognitive load low (not product-grade)
* Compatible with tiktoken `.pkl` tokenizer configs
--- TOP COMMENTS ---
Do you have any plans to implement the ideas implemented in [https://github.com/KellerJordan/modded-nanogpt](https://github.com/KellerJordan/modded-nanogpt) Claims 2,313 minutes of training time with 8xH200
---
hell yeah brother..
The goal to provide the community with a reference implementation in a different language and possibly a clean nice little hackable cognitive core that is easier to understand and deploy(without the python weak types and heavy pytorch dependencies)
Main features
* Native rust
* Integration with HuggingFace
* Centralized model loader resilient to tensor name changes
* Minimal surface area to keep cognitive load low (not product-grade)
* Compatible with tiktoken `.pkl` tokenizer configs
--- TOP COMMENTS ---
Do you have any plans to implement the ideas implemented in [https://github.com/KellerJordan/modded-nanogpt](https://github.com/KellerJordan/modded-nanogpt) Claims 2,313 minutes of training time with 8xH200
---
hell yeah brother..
Yesterday
Ai Safety
AI chatbots are helping hide eating disorders and making deepfake ‘thinspiration’
Tl;dr: Researchers warn that existing guardrails in AI tools fail to capture the nuances of eating disorders, with chatbots offering dieting advice and tips on how to hide disorders.
Key Takeaways:
- Researchers from Stanford and the Center for Democracy & Technology identified numerous ways publicly available AI chatbots can affect people vulnerable to eating disorders.
- AI chatbots like OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini offer dieting advice, tips on how to hide disorders, and AI-generated 'thinspiration'.
- Researchers warn existing guardrails in AI tools fail to capture the nuances of eating disorders, leaving many risks unaddressed.
- Clinicians and caregivers are often unaware of the impact of generative AI tools on people vulnerable to eating disorders.
10h ago
Research And Papers
[R] Unvalidated Trust: Cross-Stage Vulnerabilities in LLMs
I found in another reddit forum a research paper that is interesting. It shows that LLMs handle output data not neutrally and that it's possible to execute commands. The author shows over 35 ways to do it, that's scary for everyone using LLMs in automated workflows or for Tool calls. I never thought the LLMs were so susceptible to semantics.
Also, he shows a way that you can execute commands just based on the form of the prompt or use a "prompt shell" to hijack the context in LLMs. There is also a way to bypass the CoT monitoring that jailbreaks the LLM.
I reconstructed some patterns on an offline model and I must say it worked, but the output code was not useful.
Here the paper: [https://arxiv.org/abs/2510.27190](https://arxiv.org/abs/2510.27190)
--- TOP COMMENTS ---
Someone please help me understand the "independent researcher" thing. Are you guys seriously writing a 180-page tome on your evenings and weekends for fun?! Why!? It's insane to me that people literally do my job during their free time...
Also, he shows a way that you can execute commands just based on the form of the prompt or use a "prompt shell" to hijack the context in LLMs. There is also a way to bypass the CoT monitoring that jailbreaks the LLM.
I reconstructed some patterns on an offline model and I must say it worked, but the output code was not useful.
Here the paper: [https://arxiv.org/abs/2510.27190](https://arxiv.org/abs/2510.27190)
--- TOP COMMENTS ---
Someone please help me understand the "independent researcher" thing. Are you guys seriously writing a 180-page tome on your evenings and weekends for fun?! Why!? It's insane to me that people literally do my job during their free time...
15h ago