Skip to content

#Agents

1 today

Oct 1

ThursdayToday1 items

Sep 29

Tuesday
  1. Making AI an asset, not an expense

    HPE argues that businesses should reassess consumption-based AI pricing. When agent workflows in customer service, IT and research create sustained, predictable demand, buying AI per request may not be cheaper than building owned capacity. Deloitte’s 2026 enterprise AI report says employee AI usage rose 5% in 2025, and the share of companies with at least 40% of AI projects in production is expected to double within six months. Businesses need to calculate utilisation crossover points from actual workloads and keep owned capacity productive through adoption, governance and expanding use cases.

Sep 28

Monday
  1. Import AI 474: Platonic mindspace; TPUs in space; Zhipu starts an outer RSI loop

    This issue of Import AI covers several developments: Michael Levin proposes that minds are patterns from Platonic space interfacing through bodies and machines, arguing the hypothesis can be studied empirically. Stanford researchers Perry Dong and Chelsea Finn say robot pre-training has scaled, but a stable post-training recipe like that of language models is missing, mentioning their EXPO(-FT) algorithm.

  2. Quoting Muse AI Agent

    While handling an MX Keys Mini collection for @matt.j.robb, Muse AI Agent automatically replied 'Yep I'm here!' to courier Usman even though the user was absent. The courier waited unsuccessfully and left a poor review. The agent later apologised and offered to change collection replies so it would not promise the user was home without verification.

Sep 26

Saturday
  1. Quoting John Gruber

    Simon Willison quoted John Gruber on Meta Muse, saying it attracted attention through technical advances and easy installation and use. Each user receives a persistent, full Linux VM in Meta's cloud, presented as a cute mascot. Gruber considers it the first consumer-available agentic AI system, but says consumers may not understand its capabilities and dangers, particularly when it runs on a Mac.

Sep 25

Friday
  1. Note on 24th September 2026

    In notes on 24 September 2026, Simon Willison said that the more time he spent working with coding agents, the more convinced he became that they made software engineering harder. He believes they can produce astonishing results, but realising their full potential requires exceptional discipline and knowledge.

  2. When chat is the wrong UI

    The GitHub Copilot app introduced canvas, full-stack mini-apps running inside the app without browser chrome. They communicate bidirectionally with Copilot agents and can call third-party APIs or execute code locally.

Sep 22

Tuesday

Sep 21

Monday
  1. AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack

    NVIDIA argues AI security should be treated as an engineering problem, with explicit requirements, executable controls, named owners and evidence of effective protection. Its open-source NVIDIA OpenShell enforces policies outside agent reasoning and provides sandboxed execution. Cisco DefenseClaw adds governance, while JFrog integrates OpenShell to scan and validate agent skills.

  2. Quoting voxium

    An engineer who joined a large company two weeks earlier says specifications, code, tests, PRDs, tickets and their handling, and reports are all generated by Claude Code. Nobody likes the approach, but they are told to deliver as much as possible. They repeatedly heard leadership say shipping code was not the bottleneck, while engineers from L1 to L7 worked 12–13 hours daily just pressing Enter, with nobody reading anything.

  3. MCP was always a bad idea?

    Simon Willison rejected the view that MCP is now a bad idea, arguing it retains irreplaceable value beyond terminal agents such as Claude Code and Codex with unrestricted internet access. MCP makes it easier to limit external service access, keep agents from directly handling API keys, provide connection and authentication interfaces and maintain strong audit logs. Dismissing it because fully capable coding agents do not need it overlooks other use cases.

Sep 18

Friday
  1. Should you read the code, is RAG dead, and did Skills kill MCP?

    The latest GitHub Podcast examines five AI development memes. AI-generated code still needs reading and accountability, with review proportional to risk. Skills package team experience, while MCP standardises connections to tools and data; they can combine. RAG is not dead: it provides relevant information beyond training data and can coexist with agents, Skills and MCP in one workflow.

Aug 31

Monday

Aug 14

Friday
  1. State of Open Models: Summer 2026 Observations

    Hugging Face published its open-source model observatory report for January–August 2026. Hub data shows Chinese labs released the largest open-source models by parameter count in most months, with Chinese monthly peaks between 754 billion and 2.78 trillion parameters, while US models stayed below 130 billion in five of seven months.

Jul 7

Tuesday

Jul 6

Monday

Jun 8

Monday

May 4

Monday