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Anthropic made Claude Sonnet 5 more agentic, but also more controversial.
The new model is built to plan longer, use tools more often, check its own work, and stay inside coding loops with less hand-holding.
Sounds like an upgrade, but the reaction has been mixed.
Some users are seeing slower runs, heavier token use, and more second-guessing on simple tasks.
We broke down what changed, why it feels different, and what to know before switching.
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Your agent is probably just a workflow, and one 603-page paper proves it.
A workflow runs the steps you wrote but an agent decides its own next step.
The Hitchhiker's Guide to Agentic AI, a paper by Haggai Roitman, maps that whole gap. It treats an agent as a model inside a system, not a clever prompt.
The core idea is a loop: observe, reason, act, then stop or ask for help. Everything else is the stack you build around it to keep it safe.
It walks the full stack:
> retrieval and memory systems
> the harness and orchestration
> tools, MCP, and A2A
> multi-agent coordination and evaluation
Most tasks never need all of it. That shows when an agent is worth it.
The honest takeaway: start simple, add autonomy only when the job demands it.
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Paper: arxiv.org/abs/2606.24937
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Top AI Educational Posts of the Week (June 25 - July 2)
1. You can now turn any source into a 60-second video in NotebookLM
2. Learn how loop engineering helps AI agents build software with human feedback.
3. CS 153 at Stanford: Learn AI systems from the people building the frontier.
4. Learn LLM agents from UC Berkeley, DeepMind, Meta, and top AI researchers
5. Learn how to build a real second-brain AI assistant with RAG, agents, and LLMOps.
More details in the thread.
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/6 Decoding AI’s open-source course teaches you how to build a personal AI assistant with LLMs, agents, RAG, fine-tuning, and LLMOps.
It covers:
> Data pipelines
> Dataset generation
> Fine-tuning
> Advanced RAG
The stack includes ZenML, Opik, Comet, Unsloth, MongoDB, Hugging Face, and OpenAI.
A practical path for building a real AI assistant over your own knowledge base.
nitter.app/DanKornas/status/20722…
Your second brain needs more than a chatbot demo
second-brain-ai-assistant-course is an open-source course repo from Decoding AI for builders who want to create a personal knowledge-base assistant with LLMs, agents, RAG, and LLMOps.
It helps you move from scattered notes to an end-to-end assistant by walking through six modules: data pipelines, dataset generation, fine-tuning, deployment, advanced RAG, and agentic inference/observability.
Key features:
• Six-module path – covers architecture, ETL, fine-tuning, deployment, RAG, and LLMOps
• Offline + online apps – separates ML/data pipelines from the assistant inference pipeline
• Notion-friendly data flow – uses a Second Brain knowledge base, with a public snapshot so a Notion account is optional
• Real LLMOps tooling – includes ZenML, Opik, Comet, Unsloth, MongoDB, Hugging Face, and OpenAI
• Builder-first setup – module docs, runnable code, uv/ruff, and Docker infrastructure included
It’s open-source (MIT license).
Link in the reply 👇
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Thanks for reading!
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The quiet signal under all three: none of them is a new frontier model. They're infrastructure for a world where the models already outrun our ability to check them.
AlphaSignal covers shifts like this before they're obvious: alphasignal.ai/newsletter
GeneBench-Pro: openai.com/index/introducing…
Science: anthropic.com/news/claude-sc…
paper: arxiv.org/abs/2606.28277
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/7 Line the three up and the pattern is hard to miss.
> Claude Science checks the work as it's produced.
> GeneBench-Pro measures whether the work can be trusted.
> PAT catches the errors before publication.
Generation, evaluation, verification!
Three labs independently decided the bottleneck in AI science is no longer producing results. It's confirming them.
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/6 Google's answer is PAT, the Paper Assistant Tool, an agent that reviews a full manuscript before a human referee ever sees it.
It ingests the whole paper, validates the experiments, and checks the math. On the SPOT benchmark for catching mathematical errors, the old state of the art detected 21.1%. Gemini 3.1 Pro zero-shot gets 55.2%. PAT built on the same model gets 89.7%.
The lift comes from inference-time scaling, not a bigger model.
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/5 The interesting part of GeneBench-Pro is how the models fail.
Per the paper, they complete large parts of the workflow, then break at a specific point: they identify a local diagnostic signal but don't propagate it into the analysis decision it should change. The paper's words are a gap between noticing and acting.
The models see the problem in the data. They just don't let it change what they do next.
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/4 OpenAI's answer is GeneBench-Pro, and it's the most sobering of the three.
129 real problems across genomics and biomedicine, each with a downstream decision that depends on getting the analysis right. Best score, GPT-5.6 Sol at max reasoning, is 28.7%. Sol Pro reaches 31.5% in separate runs. The strongest non-GPT model, Claude Opus 4.8, gets 16.0%.
The frontier is failing two out of three of these.
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/3 Anthropic's answer is Claude Science, a workbench that pulls the scattered tools of research into one environment.
The useful part isn't the chat box. It's the checking. It connects to UniProt, PDB, Ensembl, ChEMBL, runs its own consistency passes, and flags figures that don't match the underlying data. In the demo it caught a citation where a PubMed ID was assigned to two different methods papers, and corrected it mid-plan.
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/2 Start with the pressure. Submissions to ICLR, ICML, and NeurIPS went from 17,051 in 2020 to an estimated 73,883 in 2026. Up about 63% in the last year alone.
Human peer review is roughly linear. You add reviewers one at a time. The output of AI research assistants is not linear. That gap is the thing all three launches are reacting to.
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In one two-week window, Anthropic, OpenAI, and Google each shipped scientific tooling. Different products, no coordination.
Anthropic built a research workbench. OpenAI built a benchmark. Google built a paper reviewer. They're all aimed at the same problem nobody names: AI can now generate science faster than anyone can check it.
More details in thread:
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