Source: For You timeline (33 tweets) Method: Full heuristics including major AI companies, scientific papers, DSPy skip
Linear's CEO just described the biggest shift in product team structure since Agile.
For decades, product work meant: PM defines requirements → designers create specs → engineers translate to code. The middle step, translation, absorbed 70% of the time and created most of the friction.
Product/org insight on AI changing team structure. Concrete observation from Linear.
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Data Agent - Made by the LangChain Community
An open-source NL2SQL platform that converts natural language to SQL across 6 databases. Built with LangChain SQLDatabase and LangGraph multi-agent architecture for routing, generation, and validation.
LangChain releasing NL2SQL tool. Relevant for data extraction workflows.
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Recursive Language Models is now on arXiv. @a1zhang worked hard to catch a Dec 31st, 2025 timestamp!
Most people (mis)understand RLMs to be about LLMs invoking themselves. The deeper insight is LLMs interacting with their own prompts as objects.
RLM paper on arXiv. You found PrimeIntellect's RLM work "VERY INTERESTING" - this is the paper.
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Use natural conversational AI to create a partner, not just a chatbot.
Watch this demo to learn how to build a real-time proactive advisor agent—which showcases two capabilities: Dynamic Knowledge Injection + Dual Interaction Modes.
Full source code → https://t.co/s5rVAFWEdW
Google Cloud showing agent architecture with source code. Major company, agent-related.
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Understanding mHC: Manifold-Constrained Hyper-Connections. [visualization]
DeepSeek paper explained with visuals. Architecture innovation.
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Here's proof that Claude Code can write an entire empirical polisci paper.
To validate my claim that AI agents are coming for polisci "like a freight train", today I had Claude Code fully replicate and extend an old paper of mine estimating the effect of universal vote-by-mail.
Claude Code writing complete research paper - capability demonstration beyond just coding.
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250 ms to first audio chunk for @resembleai chatterbox turbo locally 🔥
Really awesome example built @KingBootoshi with MLX-Audio!
And yes, you have 24/7 voice agents that sound how you want without breaking the bank.
Repo: https://t.co/STF50gFoWW
Fast local voice AI. Relevant to local inference, audio models.
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A useful repo on how to build a production-ready agentic AI system
You have to watch two things all the time: • Agent behavior → reasoning, tool use, memory, safety • System reliability and performance → latency, uptime, cost, recovery under load
Production agent systems repo. Relevant to agent development.
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Claude Code isn't just for coding.
I fed it my raw DNA data from an ancestry test and used it to find health related genes I should keep an eye on.
The file is massive, but its ability to search what matters makes it possible.
Claude Code non-coding use case. Practical capability demonstration.
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First day of 2026 well spent: Building an agent on MiniMax M2.1, integrating a VLM for VLA-based robotic arm control in simulation.
Major AI company (MiniMax) showing agent + VLM integration.
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Introducing Claude HUD!
A Claude Code plugin that shows you: · context remaining in the session · what tools are executing · which subagents are running · claude's to-do list progress
Easy installation guide below ↓
Claude Code plugin for workflow visibility. Practical tool.
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Memory for AI Agents is still in its early phases.
But it's crucial for more capable multi-agent systems.
This paper provides an interesting perspective.
Memory isn't just storage. It's the cognitive bridge between past experience and future decisions.
However, most AI agent systems still treat memory as simple retrieval.
Agent memory research. Relevant to agent development.
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Original:
"Recursive Language Models" - A potentially big direction for LLMs in 2026 from MIT researchers. In their approach, a prompt isn't "run" directly, instead it's stored as a variable in an external Python REPL, and the language model writes code to inspect/slice/decompose that long prompt.
Rewritten: MIT paper on Recursive Language Models. Core idea: prompts stored as variables in external Python REPL, not executed directly. LLM writes code to inspect/slice/decompose prompts. Enables new control flow patterns for LLM execution.
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I highly recommend this article for anyone who still thinks that LLMs are still "only predicting the next token". It's long and unsettling but worth the read.
Article on LLM capabilities beyond token prediction. Unknown quality.
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Claude Code battle: GLM 4.7 vs MiniMax 2.1 creating fireworks effect. I bet 2026 will be an amazing year for AI!
Local model comparison in Claude Code. Relevant to local LLM.
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"Good product work is seeking clarity. In this era, directing and managing agent work becomes the craft. Writing code is less like constructing a solution and more like steering one into existence."
Product work insight on AI era. Abstract but from credible source.
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| # | Author | Reason | Link |
|---|---|---|---|
| 1 | @karrisaarinen | Just a link, no context | view |
| 2 | @m_sirovatka | Vague praise ("next frontier") | view |
| 3 | @DilumSanjaya | 3D graphics workflow | view |
| 4 | @kepano | Just asking, no content | view |
| 5 | @ProfTomYeh | ML fundamentals, below expertise | view |
| 6 | @d0wnsideofme | Zero content | view |
| 7 | @TensorTonic | ML fundamentals, below expertise | view |
| 8 | @sciencegirl | Entertainment/design | view |
| 9 | @GithubProjects | Generic utility | view |
| 10 | @leerob | Vague BS ("Now what?") | view |
| 11 | @HenrypenmanXD | Team insight, too vague | view |
| 12 | @shiri_shh | Just a link | view |
| 13 | @carmenansio | UI/UX demo | view |
| 14 | @a1zhang | Meta-announcement, no content | view |
| 15 | @elithrar | Vague praise for something | view |
| 16 | @vamsibatchuk | Game project | view |
| 17 | @iruletheworldmo | Just a link | view |
| Count | % | |
|---|---|---|
| Surface | 12 | 36% |
| Rewrite | 1 | 3% |
| Maybe | 3 | 9% |
| Skip | 17 | 52% |
Higher surface rate this time due to more agent/AI company content in timeline.