Artificial intelligence

We will make the new 𝕏 algorithm, including all code used to determine what organic and advertising posts are recommended to users, open source in 7 days. This will be repeated every 4 weeks, with comprehensive developer notes, to help you understand what changed.

Tailwind lays of 75% of their team. the reason is so ironic: > their css framework became extremely popular w AI coding agents, 75m downloads/mo > that meant nobody would visit their docs where they promoted paid offerings > resulting in 40% drop in traffic & 80% revenue loss

We're feeding AI our best work for free, and nobody is talking about what happens next. AI will scrape every blog and social media post you publish. AI will scrape every single open-source code you share. AI will scrape every tutorial you record. And then, they will sell this info back to you in the form of tokens and soon, ads. The economics of the future of content creation are broken. If we don't fix this, we will regret it.

ICYMI - Claude Code in the Claude Desktop app! Benefits: → Visual session management instead of terminal tabs → Parallel sessions via git worktrees → Run locally or in the cloud → One-click to open in VS Code or CLI Same Claude Code. Better ergonomics.

The spread between how one-person dev teams are building software is fascinating: 1. Multiple agents, shipping at inference speed, not reading the code (but very involved designing it) - some 2. Heavy use of AI IDEs and a single AI agent - many 3. Mostly in the IDE - fewer

You guys realize that all software is about to be free, right? And software is presently the most valuable capital asset, right? Guys?

We need a shorthand way of saying: "An AI did the work, but I vouch for the result" Saying "I did it" feels slightly sketchy, but saying "Claude did it" feels like avoiding responsibility

Welcome to "Situation Monitor" > Global Activity Monitor > @tbpn livestream > Intel Feed > Tech/Finance/Politics newsfeed > Stocks/Crypto > @Polymarket predictions > Tech layoffs tracker > AI Race news > Is the Fed printer on? > Venezuela + Greenland https://hipcityreg.github.io/situation-monitor/…

NEWS: Boston Dynamics has just released a new video of its upgraded next-generation humanoid robot called Atlas. • 4 hour battery. Self-swappable for continuous operation • 6 feet 2 inches tall • Weight: 198 lbs • 56 total degrees of freedom • Now fully electric, ditching older hydraulic systems • New lightweight mix of aluminum and titanium components • 110 lbs weight capacity (66 lbs sustained) • Can reach up to 7.5 ft • Constantly evaluates its surroundings and adjusts its posture, balance, and grip in real time • Hands that can reconfigure as needed. Tactile sensors feed data back into the system, helping apply the right amount of force • Brain is powered by Nvidia chips

10 days into 2026: - Terence Tao announces GPT & Aristotle solve Erdős problem autonomously - Linus Torvalds concedes vibe coding is better than hand-coding for his non-kernel project - DHH walks back “AI can’t code” from Lex podcast 6 months later An acceleration is coming the likes of which humanity has never experienced before

We just open sourced the code-simplifier agent we use on the Claude Code team. Try it: claude plugin install code-simplifier Or from within a session: /plugin marketplace update claude-plugins-official /plugin install code-simplifier Ask Claude to use the code simplifier agent at the end of a long coding session, or to clean up complex PRs. Let us know what you think!

I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour.

opus 4.5 is the model for sync work gpt 5.2 is the one for async

AI agents will be a big part of how we shop in the not-so-distant future. To help lay the groundwork, we partnered with Shopify, Etsy, Wayfair, Target and Walmart to create the Universal Commerce Protocol, a new open standard for agents and systems to talk to each other across every step of the shopping journey. And coming soon, UCP will power native checkout so you can buy directly on AI Mode and the @Geminiapp.

DeepSeek founder Liang Wenfeng’s quantitative hedge fund generated returns of more than 50% last year

Claude Code 2.1.0 is officially out! claude update to get it We shipped: - Shift+enter for newlines, w/ zero setup - Add hooks directly to agents & skills frontmatter - Skills: forked context, hot reload, custom agent support, invoke with / - Agents no longer stop when you deny a tool use - Configure the model to respond in your language (eg. Japanese, Spanish) - Wildcard support for tool permissions: eg. Bash(*-h*) - /teleport your session to http://claude.ai/code - Overall: 1096 commits https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md… If you haven't tried Claude Code yet: https://code.claude.com/docs/en/setup Lmk what you think!

Anthropic launched Claude for Healthcare with HIPAA-ready products and expanded Claude for Life Sciences with new connectors ranging from clinical trial management to regulatory operations - Claude for Healthcare connects to Centers for Medicare and Medicaid Services Coverage Database, International Classification of Diseases 10th Revision codes, and National Provider Identifier Registry, with new Agent Skills for FHIR development and a sample prior authorization review skill that can be customized to organizations' policies - US Claude Pro and Max plan subscribers get beta access to HealthEx and Function connectors now, with Apple Health and Android Health Connect integrations rolling out in beta this week on iOS and Android apps for accessing lab results and health records - Claude for Life Sciences adds connectors to Medidata for trial data and site performance, ClinicalTrials[.]gov, ToolUniverse with 600+ vetted scientific tools, bioRxiv and medRxiv preprint servers, Open Targets, ChEMBL, and Owkin Pathology Explorer for tissue image analysis - New Agent Skills for scientific problem selection, converting instrument data to Allotrope, scVI-tools and Nextflow deployment for bioinformatics, and a sample skill for clinical trial protocol draft generation with endpoint recommendations accounting for regulatory pathways, competitive landscape, and FDA guidelines - Anthropic is hosting "The Briefing: Healthcare and Life Sciences", a free livestreamed virtual event on January 12 at 11:30 AM PST with Anthropic leadership and customer perspectives on AI in healthcare

> Tailwind lays of 75% of their team because of AI as they underwent a 80% revenue loss > 24 hours later sponsors arrive from big places internet is good

1 in every 5 founders I meet in the Bay Area is building an observability platform for agents. There are so many observability platforms, yet the least mature part of building an agent is still telemetry. Everyone is trying to solve the wrong problem with observability. Visibility is the easiest piece. The hard part is analyzing and understanding what you’re observing. I’ve spoken to teams recording 100k+ traces every single day. What are they doing with those traces? Literally nothing. Because it’s impossible to read and summarize 100,000 traces at any human scale. So stop vibecoding those stupid dashboards, and how about you re-center the problem from first principles?

Great paper on Agentic Memory. LLM agents need both long-term and short-term memory to handle complex tasks. However, the default approach today treats these as separate components, each with its own heuristics, controllers, and optimization strategies. But memory isn't two independent systems. It's one cognitive process that decides what to store, retrieve, summarize, and forget. This new research introduces AgeMem, a unified framework that integrates long-term and short-term memory management directly into the agent's policy through tool-based actions. Instead of relying on trigger-based rules or auxiliary memory managers, the agent learns when and how to invoke memory operations: ADD, UPDATE, DELETE for long-term storage, and RETRIEVE, SUMMARY, FILTER for context management. It uses a three-stage progressive RL strategy. First, the model learns long-term memory storage. Then it masters short-term context management. Finally, it coordinates both under full task settings. To handle the fragmented experiences from memory operations, they design a step-wise GRPO (Group Relative Policy Optimization) that transforms cross-stage dependencies into learnable signals. The results across five long-horizon benchmarks: > On Qwen2.5-7B, AgeMem achieves 41.96 average score compared to 37.14 for Mem0, a 13% improvement. > On Qwen3-4B, the gap widens: 54.31 vs 44.70. Adding long-term memory alone provides +10-14% gains. > Adding RL training adds another +6%. > The full unified system with both memory types achieves up to +21.7% improvement over no-memory baselines. The unified memory management through learnable tool-based actions outperforms fragmented heuristic pipelines, enabling agents to adaptively decide what to remember and forget based on task demands. Paper: https://arxiv.org/abs/2601.01885 Learn to build effective AI agents in our academy: https://dair-ai.thinkific.com