π΄ High Significance
Model Releases
π΄ Qwen3.6-27B vs Coder-Next β score 96
Sources: reddit/r/LocalLLaMA
Burned about 20 hours of side-by-side compute on my two RTX PRO 6000 Blackwells trying to get a definitive answer on which of these two models was clearly better. As with many things in life, after many tokens and kWhs later the answer was "it depends."
These models in the aggregate are actually cr
π΄ Karpathy's MicroGPT running at 50,000 tps on an FPGA β score 81
Sources: reddit/r/LocalLLaMA
Sure, it's only 4,192 parameters, but it's a start. Project write-up here: https://v2.talos.wtf/ and github repository here: https://github.com/Luthiraa/TALOS-V2
Some of the speed comes from having the weights onboard, rather than
π΄ GPT 5.5 just leaked its chain of thought to me in codex, and it looks like an idea from 5 months ago in this sub. β score 73
Sources: reddit/r/LocalLLaMA
https://www.reddit.com/r/LocalLLaMA/comments/1p0lnlo/make_your_ai_talk_like_a_caveman_and_decrease/
In the middle of a project I'm working on, I got this output from GPT 5.5-medium via codex:
Implemented the narrower fix in Homm3ImportUnitPreviewModelHook.cs? Need absolute path. Need know cwd abso
Developer Tools
π΄ TauricResearch/TradingAgents β TradingAgents: Multi-Agents LLM Financial Trading Framework β score 99
Sources: github_trending
TradingAgents: Multi-Agents LLM Financial Trading Framework
π΄ ruvnet/ruflo β π The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration β score 97
Sources: github_trending
π The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration
π΄ Anyone can create an AI Agent now β score 94
Sources: reddit/r/AIAgents
Super Agents - Anyone can create the agents now
Last 6 weeks I've been building something I wish existed when I started: a no-code AI agent platform where anyone β not just
developers β can build their own production-grade bots in an afternoon.
π΄ 1jehuang/jcode β Coding Agent Harness β score 93
Sources: github_trending
Coding Agent Harness
π΄ AIDC-AI/Pixelle-Video β π AI ε
¨θͺε¨ηθ§ι’εΌζ | AI Fully Automated Short Video Engine β score 91
Sources: github_trending
π AI ε ¨θͺε¨ηθ§ι’εΌζ | AI Fully Automated Short Video Engine
Omitted 7 additional developer tools items from the main section; see raw data and source-specific sections below.
Research Papers
π΄ Efficient Training on Multiple Consumer GPUs with RoundPipe β score 95
Sources: huggingface
Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP
π΄ Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows β score 85
Sources: huggingface
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow deman
π΄ Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence β score 75
Sources: huggingface
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni delivers consistent accuracy improvements over its predecessor, Nemotron Nano V2 VL, across all modalities, ena
Other Signals
π΄ Group averages obscure how an individual's brain controls behavior: study β score 75
Sources: hackernews
π‘ Notable
Model Releases
π‘ I made a visualizer for Hugging Face models β score 68
Sources: reddit/r/LocalLLaMA
I built hfviewer.com, a small tool for visually exploring Hugging Face model architectures.
You can paste a Hugging Face URL and get an interactive visualization of the architecture, which can make it easier to understand how different models are structured and compare th
π‘ **[@OpenAI: One week since the launch of GPT-5.5, and itβs already our strongest model launch yet.
API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven d](https://x.com/OpenAI/status/2050250926888468929)** β score 60
Sources: twitter_rss
One week since the launch of GPT-5.5, and itβs already our strongest model launch yet.
API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven days as enterprise demand for agentic coding tools keeps climbing.
π‘ **[@xai: Voice Cloning is now live via the xAI API!
Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, vide](https://x.com/xai/status/2050355373052223585)** β score 60
Sources: twitter_rss
Voice Cloning is now live via the xAI API!
Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, video game characters, and more.
http://x.ai/news/grok-custom-voices
π‘ **[@xai: Introducing Grok Voice Think Fast 1.0
A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy.
It takes the top spot on the Tau Voice Bench and](https://x.com/xai/status/2047441173569216721)** β score 60
Sources: twitter_rss
Introducing Grok Voice Think Fast 1.0
A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy.
It takes the top spot on the Tau Voice Bench and handles real-world messiness like noise, accents, and interruptions better than any other model in
π‘ **[@MistralAI: π Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI.
π Enterprise teams have capable models. What they don't have is a way to run them reliably in prod](https://x.com/MistralAI/status/2049128071874179091)** β score 60
Sources: twitter_rss
π Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI.
π Enterprise teams have capable models. What they don't have is a way to run them reliably in production. That's the gap Workflows fills. It takes AI-powered business processes from prototype to pro
Omitted 4 additional model releases items from the main section; see raw data and source-specific sections below.
Developer Tools
π‘ iOfficeAI/AionUi β Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | π Star if you like it! β score 66
Sources: github_trending
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | π Star if you like it!
π‘ LearningCircuit/local-deep-research β Local Deep Research achieves ~95% on SimpleQA benchmark (tested with Qwen 3.6). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local & Encrypted. β score 64
Sources: github_trending
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with Qwen 3.6). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local & Encrypted.
π‘ vas3k/TaxHacker β Self-hosted AI accounting app. LLM analyzer for receipts, invoices, transactions with custom prompts and categories β score 61
Sources: github_trending
Self-hosted AI accounting app. LLM analyzer for receipts, invoices, transactions with custom prompts and categories
π‘ Q00/ouroboros β Agent OS: Stop prompting. Start specifying. β score 59
Sources: github_trending
Agent OS: Stop prompting. Start specifying.
π‘ harvard-edge/cs249r_book β Machine Learning Systems β score 57
Sources: github_trending
Machine Learning Systems
Omitted 9 additional developer tools items from the main section; see raw data and source-specific sections below.
Enterprise Adoption
π‘ **[@swyx: Much respect to @tokengobbler who shutdown Vibe-kanban live onstage at AIE Europe - still with 30,000 MAU, and still living on as an open source project.
"Everyone who is making money is doing 2 thin](https://x.com/swyx/status/2050753293601935777)** β score 50
Sources: twitter_rss
Much respect to @tokengobbler who shutdown Vibe-kanban live onstage at AIE Europe - still with 30,000 MAU, and still living on as an open source project.
"Everyone who is making money is doing 2 things: selling to enterprise, and reselling tokens. We were doing neither."
surprisingly not the firs
Research Papers
π‘ Step-level Optimization for Efficient Computer-use Agents β score 65
Sources: huggingface
Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfaces instead of relying on brittle, application-specific integrations. Despite recent advances in benchmark performance, strong computer-use agents r
π‘ Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models β score 55
Sources: huggingface
Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However, whether fundamental reasoning patterns, such as induction, deduction, and abduction, can be decou
π‘ Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization β score 45
Sources: huggingface
Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are sim
Other Signals
π‘ Real World Physics-Informed AI Applications [D] β score 69
Sources: reddit/r/MachineLearning
I'm curios to find any real-world applications of physics-informed AI.
Conventional AI, talking only about Neural Networks, have already become something casual, they are in hundreds of tools/services we use daily. But I'm curios, apart from academia, are there industries/fields where physics-infor
π‘ If you've been waiting to try local AI development, please try it β score 65
Sources: reddit/r/LocalLLaMA
I have snobbishly long felt that the local models were not 'up to my standards' for local development, or otherwise able to compete with GHCP, Claude Code, Cursor etc.
Boy was I wrong. With the rapid increase of usage constraints and enshittification of plans all the cloud providers are starting to
π‘ Anyone submit ML articles to ACM journals (eg. TOPML or TIST)? [D] β score 56
Sources: reddit/r/MachineLearning
Have any of you submitted ML articles to ACM journals (eg. TOPML or TIST)? How long did the process take, and were the reviews high-quality? How does it compare to other journals (eg. TMLR) in terms of difficulty? Thanks.
π‘ Should I follow-up with the editor for a TMLR paper awaiting final decision? [D] β score 44
Sources: reddit/r/MachineLearning
Hi there,
I have a (long) paper that's been under review at TMLR for a while (submitted in October). After the reviews came in (mostly positive), we addressed the reviewers concerns, wrote rebuttals, and had a notification from the system according to which the final recommendations from the revi
π‘ Open Weights Models Hall of Fame β score 42
Sources: reddit/r/LocalLLaMA
I read a lot of "whengguf" type posts. I think we should sometimes stop and be grateful.
I want to say big thanks to all of the people and companies who gave us so much fun and productivity, sacrificing a lot sometimes; but also to companies who gave us models as by-product of their strategy, too.
π’ Incremental
Model Releases
π’ What a time to be alive from 1tk/sec to 20-100tk/sec for huge models β score 27
Sources: reddit/r/LocalLLaMA
https://www.reddit.com/r/LocalLLaMA/comments/1eb6to7/llama_405b_q4_k_m_quantization_running_locally/
[https://www.reddit.com/r/LocalLLaMA/comments/1ebbgkr/llama_31_405b_q5_k_m_runnin
Developer Tools
π’ junhoyeo/tokscale β π°οΈ A CLI tool for tracking token usage from OpenCode, Claude Code, π¦OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! β’ π
Global Leaderboard + 2D/3D Contributions Graph β score 39
Sources: github_trending
π°οΈ A CLI tool for tracking token usage from OpenCode, Claude Code, π¦OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! β’ π Global Leaderboard + 2D/3D Contributions Graph
π’ njbrake/agent-of-empires β Manage multiple Claude Code, OpenCode agents from either TUI or Web for easy access on mobile. Also supports Mistral Vibe, Codex CLI, Gemini CLI, Pi.dev, Copilot CLI, Factory Droid Coding. Uses tmux and git worktrees. β score 36
Sources: github_trending
Manage multiple Claude Code, OpenCode agents from either TUI or Web for easy access on mobile. Also supports Mistral Vibe, Codex CLI, Gemini CLI, Pi.dev, Copilot CLI, Factory Droid Coding. Uses tmux and git worktrees.
π’ xingkongliang/skills-manager β A lightweight desktop app to manage, sync, and organize AI agent skills across 15+ coding tools β Cursor, Claude Code, Codex, Copilot, and more. β score 34
Sources: github_trending
A lightweight desktop app to manage, sync, and organize AI agent skills across 15+ coding tools β Cursor, Claude Code, Codex, Copilot, and more.
π’ Built a efficient and fast MRI compression program called KMRI [P] β score 25
Sources: reddit/r/MachineLearning
KMRI is chunk-based MRI compression format for .nii files (Python + Zstd and C++).
Got strong compression on synthetic MRI-like volumes, especially smooth data (up to ~900Γ in best case scenarios due to zero-block skipping).
Check it out at [https://github.com/Kiamehr5/KMRI](https://github.com/K
π’ Now you can manage AI agents by assigning them to your team members or employees β score 25
Sources: reddit/r/AIAgents
Just rolled out a powerful newΒ MembersΒ feature for all our AI agents (OpenClaw, Hermes, and future products).
What it does:
When you buy an AI agent plan, you can now:
- Invite team members (by email or username) toΒ specific agents only
- Give themΒ granular permissionsΒ β e.g. view c
Omitted 6 additional developer tools items from the main section; see raw data and source-specific sections below.
Infrastructure & Compute
π’ Gemma 4 E2B runs surprisingly well on my 8GB Android phone, so I built a private voice notes app around it. β score 0
Sources: reddit/r/LocalLLaMA
Been running Gemma 4 E2B locally on my OnePlus CE 5 (8GB RAM) for a few months. Chat quality is fine for the size. What surprised me was JSON output. Short input, give it a structured prompt, you get clean parse able JSON back. Way better than I expected from a 2.4GB model on a phone.
Got me thinki
Research Papers
π’ Instruction-Guided Poetry Generation in Arabic and Its Dialects β score 35
Sources: huggingface
Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language Models (LLMs) has primarily focused on analysis tasks such as
π’ ViPO: Visual Preference Optimization at Scale β score 25
Sources: huggingface
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naiv
π’ FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption β score 10
Sources: huggingface
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as retrieval-augmented generation, autonomous agents, and AI assistants. However, security remains a major concern for their widespread deployment, with th
π’ Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains β score 10
Sources: huggingface
Foundation models are routinely fine-tuned for use in particular domains, yet safety assessments are typically conducted only on base models, implicitly assuming that safety properties persist through downstream adaptation. We test this assumption by analyzing the safety behavior of 100 models, incl
Other Signals
π’ A Qwen finetune, that feels VERY human β score 35
Sources: reddit/r/LocalLLaMA
Hello guys,
So TL;DR, I was asked by multiple people to make an Assistant_Pepe_32B version, but the best base model contender was Qwen3-32B, a model that is very hard to tune on anything other than STEM.
The concept of Assistant_Pepe is an assistant without a typical 'assistant brain', that is
π’ UAI Reviews disappeared [D] β score 25
Sources: reddit/r/MachineLearning
Did everyone elseβs reviews disappear on their submissions?
π’ The Oscars just banned AI from winning acting and writing awards β score 25
Sources: hackernews
π’ RTX A5000 Pro Balckwell 48GB β score 19
Sources: reddit/r/LocalLLaMA
What do people think about this card for an enthusiast? With 48GB. You can fit qwen 27B q8 with context. It's still pricy, I get that. But the 48 GB seems nice. The next step up would be almost double the price. $4500 vs $9000.
I would use this for finetune and inference.
I like the idea of keepin
π’ I got tired of copy-pasting the same skills directories across 8 projects, so I built a sync'd registry for them β score 5
Sources: reddit/r/AIAgents
Hey folks,
Quick context on me: I run a handful of personal projects plus some client work, all using Claude Code with, more or less, the same core set of skills. My deploy flow, my code-review preferences, a debugging skill I keep refining, etc. Every time I tweaked one in repo A, I had to
Omitted 1 additional other signals items from the main section; see raw data and source-specific sections below.
π Trending Repos
| Repo | Description | Stars Today | Language |
|---|---|---|---|
| TauricResearch/TradingAgents | TradingAgents: Multi-Agents LLM Financial Trading Framework | 3315 | python |
| ruvnet/ruflo | π The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration | 1834 | typescript |
| 1jehuang/jcode | Coding Agent Harness | 587 | rust |
| AIDC-AI/Pixelle-Video | π AI ε ¨θͺε¨ηθ§ι’εΌζ | AI Fully Automated Short Video Engine | 478 | python |
| virattt/dexter | An autonomous agent for deep financial research | 446 | typescript |
| firecrawl/firecrawl | π₯ The API to search, scrape, and interact with the web for AI | 439 | typescript |
| Hmbown/DeepSeek-TUI | Coding agent for DeepSeek models that runs in your terminal | 389 | rust |
| czlonkowski/n8n-mcp | A MCP for Claude Desktop / Claude Code / Windsurf / Cursor to build n8n workflows for you | 264 | typescript |
| cocoindex-io/cocoindex | Incremental engine for long horizon agents π Star if you like it! | 196 | python |
| iOfficeAI/AionUi | Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | π Star if you like it! | 144 | typescript |
π New Papers
| Title | Category | Hotness | Link |
|---|---|---|---|
| Efficient Training on Multiple Consumer GPUs with RoundPipe | research_paper | 35 | Open |
| Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows | research_paper | 31 | Open |
| Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence | research_paper | 18 | Open |
| Step-level Optimization for Efficient Computer-use Agents | research_paper | 14 | Open |
| Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models | research_paper | 7 | Open |
| Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization | research_paper | 5 | Open |
| Instruction-Guided Poetry Generation in Arabic and Its Dialects | research_paper | 4 | Open |
| ViPO: Visual Preference Optimization at Scale | research_paper | 3 | Open |
| FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption | research_paper | 2 | Open |
| Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains | research_paper | 2 | Open |
π¦ Twitter/X Highlights
| Account | Tweet Summary |
|---|---|
| OpenAI | One week since the launch of GPT-5.5, and itβs already our strongest model launch yet. API revenue is growing more than 2x faster than any prior release, while Codex doubled revenue in under seven days as enterprise demand for agentic coding tools keeps climbing. Post |
| xai | Voice Cloning is now live via the xAI API! Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, video game characters, and more. http://x.ai/news/grok-custom-voices Post |
| xai | Introducing Grok Voice Think Fast 1.0 A state-of-the-art voice model built for complex, multi-step workflows with snappy responses and high accuracy. It takes the top spot on the Tau Voice Bench and handles real-world messiness like noise, accents, and interruptions better than any other model in the world. https://x.ai/news/grok-voice-think-fast-1 Post |
| MistralAI | π Today, we're releasing the public preview of Workflows, the orchestration layer for enterprise AI. π Enterprise teams have capable models. What they don't have is a way to run them reliably in production. That's the gap Workflows fills. It takes AI-powered business processes from prototype to production, with the durability, observability, and fault tolerance that production actually requires. Leading organisations like ASML, ABANCA, CMA-CGM, France Travail, La Banque Postale, Moeve, and many others are already using Workflows to automate critical processes. Post |
| OpenAI | Bring your workflow to Codex in just a few clicks. Import settings, plugins, agents, project configuration, and more so you can keep working with fewer interruptions. Your move. Post |
| MistralAI | Mistral AI made the TIME100 Most Influential Companies list for 2026 β and the top 10 for AI. Why we're proud: customers run frontier models in production on their own terms, on their own infrastructure. Thank you to our customers for their trust and for joining us on the journey. Grateful to our incredible team members around the world and congrats to all the businesses recognized this year. Learn more at: https://time.com/collection/time100-most-influential-companies/2026/mistral/ #TIME100Companies #TIME100CompaniesIndustryLeader Post |
| karpathy | Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was impossible with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base and 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors. Post |
| swyx | Much respect to @tokengobbler who shutdown Vibe-kanban live onstage at AIE Europe - still with 30,000 MAU, and still living on as an open source project. "Everyone who is making money is doing 2 things: selling to enterprise, and reselling tokens. We were doing neither." surprisingly not the first company to shutter at AIE but there's a lot to learn from the process and the software engineering retrospective from 2021-2025 will stick in my mind! Post |
| simonw | I released LLM 0.32a0 this morning, a major backwards-compatible refactor of my LLM Python library and CLI tool for working with language models - the new changes should help LLM work better with reasoning models and other new frontier capabilities https://simonwillison.net/2026/Apr/29/llm/ Post |