for anyone curious, this was the result of many experiments bouncing around but this version uses… initial ideation: chatgpt for ideas/
@replit for quick build
final repo build: opus 4.7 prompting claude code
pypi testing: ran so many tests w @replit who wrote reports that I
LLMS
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AI-Powered Development Workflow: From Ideation to Testing
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Building ‘Gemini for Science’ with the scientific community
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We're building Gemini for Science with and for the scientific community. In collaboration with 100+ institutions and a trusted tester community that ranges from PhD students to Nobel laureates, we want to make sure this tech is responsible and rigorous enough to tackle real-world
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Science Skills bundle integrates life‑science models with agentic platforms
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We are also launching Science Skills, a specialized bundle that integrates insights from 30+ major life science models and databases with agentic platforms like @Antigravity to allow researchers to perform complex, manual workflows in minutes. To learn more on how to use Science
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ElevenLabs unveils Speech Engine: Turn chat agents into voice agents with one prompt.
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Introducing Speech Engine.
— ElevenLabs (@ElevenLabs) 20 mai 2026
Developers can now turn their existing chat agent into a full voice agent with one prompt.
Speech Engine combines our leading speech, transcription, and voice orchestration models into a single pipeline – all custom built to work best together. pic.twitter.com/WSWM7nppwdIntroducing Speech Engine. Developers can now turn their existing chat agent into a full voice agent with one prompt. Speech Engine combines our leading speech, transcription, and voice orchestration models into a single pipeline – all custom built to work best together.
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Why larger context windows can degrade AI agent performance
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After a long 2-week vacation, here's finally a new video! Let's finally explore why bigger context windows make your agents worse, and, as a bonus, 10 context management techniques that actually work! Watch it here: https://
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Anthropic conference: agents orchestrating other agents
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Hier 19 Mai, Anthropic a tenu une grosse conférence à Londres.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 20 mai 2026
6 ingénieurs qui ont créé Claude ont partagé ce qui va changer ta façon de builder pour toujours.
Gardez-la précieusement en signet 🔖
Et personne n'en parle encore:
→ Des agents qui se pilotent entre eux… pic.twitter.com/Qae7TYuopMHier 19 Mai, Anthropic a tenu une grosse conférence à Londres. 6 ingénieurs qui ont créé Claude ont partagé ce qui va changer ta façon de builder pour toujours. Gardez-la précieusement en signet Et personne n'en parle encore: → Des agents qui se pilotent entre eux
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LangSmith Sandboxes GA: Agents Get Isolated Runtime Environment
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ICYMI: LangSmith Sandboxes are GA Agents get a real filesystem, shell, and package manager. Isolated from your infra. Works with Deep Agents, Open SWE, or your own code. Auth with the same API key you already have. No new runtime to build or manage.
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AI-Powered Query-Aware Compression for Enhanced Search Accuracy
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We've productionized query-aware compression for faster, cleaner, more-accurate search. Better context is better than more context. Our system cuts context tokens up to 70% while improving answer quality.
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Virtual Town Experiment Compares Agent Behavior Across Different LLMs
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Emergence AI built five identical virtual towns and gave each one 10 agents.
— The Rundown AI (@TheRundownAI) 20 mai 2026
All had the same rules and starting conditions. The only thing that changed was the model running the agents.
15 days later, Claude Sonnet's town had zero crimes.
GPT-5 Mini's agents didn't break… pic.twitter.com/xUKw8Gc42YEmergence AI built five identical virtual towns and gave each one 10 agents. All had the same rules and starting conditions. The only thing that changed was the model running the agents. 15 days later, Claude Sonnet's town had zero crimes. GPT-5 Mini's agents didn't break
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AI agents and recursive performance optimization in code training
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This is the part of AI progress that will sneak up on people. Agents grinding away at training code, kernels, architecture tweaks, and benchmarks until they start finding real performance gains. Coding agents are struggling at this, but this breakthrough looks promising.