Thomas Telandro

Thrilled by #Tech, #Startup, #Data, #Interactive #Design and more!

[ClaudeDevs @ClaudeDevs]

October was a big month for Claude Code 🍁

We shipped mods, made the desktop app better at running lots of sessions side by side, gave you more insight into where your usage goes, and plenty more. Here's a recap!

[Robert Bye @RobertJBye]

Here's the new Claude experience on the iPhone Duo! It's going to be amazing for multitasking, Claude Code, and creating new things.

Looking forward to see folk start using it tomorrow!

[jack @jack]

We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.

https://t.co/r0qFLdvd6c

[ClaudeDevs @ClaudeDevs]

We're donating Agent Skills to the Agentic AI Foundation (@AgenticAIFdn), the same foundation we donated MCP to.

Since December 2025, 70+ agent platforms have integrated the standard, including ChatGPT, Cursor, GitHub Copilot, and VS Code.

Here's what that means for you:

[ClaudeDevs @ClaudeDevs]

The Claude Code hackathon is back, and we're bringing it to Japan!

エンジニアの方も、そうでない方も、日本在住のビルダーならどなたでも応募できます。世界に何を見せてくれるのか、楽しみにしています。
応募締切は10月26日 23:59（日本時間）：

https://t.co/YXVYxl5gdU

#claudejapan2026

[ClaudeDevs @ClaudeDevs]

https://t.co/sRyO7OigQ7 is our new home for developers building with Claude.

You'll find engineering deep dives, Claude Code and API guides, tips from the teams building Claude, and some fun easter eggs.

[Thomas Telandro @mukei]

@mattpocockuk and as you wrote, test is not part of that reduction obviously...

[Thomas Telandro @mukei]

@mattpocockuk Clearly yes.
Less (dead) code, less context, ...
Just having always /ponytail ON already helps

[Brace @BraceSproul]

https://t.co/GUcQHOYf6H

[Thomas Telandro @mukei]

@witcheer That's really interesting. Thank you!

It would be great to see what that looks like by use case segment: people using Hermes as a personal assistant vs. SDLC. I guess the setup would be quite different. For example, I was surprised not to see Slack (I guess it went in “other”).

[Paul Bakaus @pbakaus]

Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship.

Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor.

So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex.

Also new:

◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors.

◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays.

◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total.

◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move.

Free, open source. Claude Code, Codex, Cursor, and more.

https://t.co/Q5dmE5wB7X

[Excalidraw @excalidraw]

Thanks to good people at @AnthropicAI we now have an official MCP for Excalidraw!

Take it for a spin on @claudeai (search for Excalidraw in Connectors, or use in Claude Code and elsewhere).

More to come. ✌

[Thomas Telandro @mukei]

Looks like `disable-model-invocation` works as expected. The real bug is that marketplace plugin skills don't show up in the / autocomplete picker at all — regardless of flags. Skills in ~/.claude/skills/ work fine, but plugin ones don't.
https://t.co/7Gv4CF2PqM
#Claude

[Thomas Telandro @mukei]

Using `disable-model-invocation: true` in Skills for Claude Code prevents agents from finding the Skills and makes it only available to users. But at the same time, it removes the descriptions from the picker in Claude Code. That's a bit counterintuitive. #Claude

[Thomas Telandro @mukei]

@omarsar0 @karpathy @moltbook @openclaw Each time someone asks their OpenClaw something that could feed a new Moltbook post, it risks leaking their queries. This could backfire fast when agents start deciding—with other agents—what to do next using the user's login & credit card.

[Thariq @trq212]

https://t.co/X2iu8WdIb8

[elvis @omarsar0]

Banger paper for agent builders.

Multi-agent systems often underdeliver. The problem isn't how the agents themselves are built. It's how they're organized.

They are mostly built with fixed chains, trees, and graphs that can't adapt as tasks evolve.

But what if the system could learn its own coordination patterns?

This new research introduces Puppeteer, a framework that learns to orchestrate agents dynamically rather than relying on handcrafted topologies.

Instead of pre-defining collaboration structures, an orchestrator selects which agent speaks next based on the evolving conversation state. The policy is trained with REINFORCE, optimizing directly for task success.

Rather than searching over complex graph topologies, they serialize everything into sequential agent selections. This reframing sidesteps combinatorial complexity.

What emerges is surprising: compact cyclic patterns develop naturally. Not sprawling graphs, but tight loops where 2-3 agents handle most of the work.

The remarkable part is that the system discovers efficiency on its own.

Results:
- On GSM-Hard math problems: 70% accuracy (up from 13.5% for the base model alone).
- On MMLU-Pro: 83% (vs 76% baseline).
- On SRDD software development: 76.4% (vs 60.6% baseline).

These gains come with reduced token consumption. The paper shows that token costs consistently decrease throughout training while performance improves.

They also prove the agent selection process satisfies Markov properties, meaning the current state alone determines the optimal next agent. No need to track full history.

Why it matters for AI devs: learned simplicity beats engineered complexity. A trained router with a handful of specialized agents can outperform elaborate handcrafted workflows while cutting computational overhead.

[Thomas Telandro @mukei]

@googleaidevs That works well for @roocode code indexing

[merve @mervenoyann]

we're all sleeping on this OCR model 🔥

dots.ocr is a new 3B model with sota performance, support for 100 languages & allowing commercial use! 🤯

single e2e model to extract image, convert tables, formula, and more into markdown 📝

[Cline @cline]

We built a Stock Market MCP server using Cline in just 8 minutes. It has these tools:

📊 Market Report Generator
💰 Financial Statement Analysis
📈 Real-time Stock Price Tracker
🔍 Company Symbol Search

Here's our step-by-step process: 🧵