appleclan

apple clan

[Nicolas Finet @nifinet]

https://t.co/OReicScEsl

[Avid @Av1dlive]

https://t.co/iQhxxaTUrb

[Akshay 🚀 @akshay_pachaar]

https://t.co/qmo2U1T4Nl

[Google AI Developers @googleaidevs]

Building autonomous agents for scientific discovery? 🧬🤖

@GoogleDeepMind Science Skills is now available on GitHub. We've open-sourced this specialized toolkit to accelerate your agentic workflows with scientific grounding and higher token efficiency.

Download now ↓
https://t.co/cwp1HOeKvo

[Codex Changelog @Codex_Changelog]

🚀 Codex CLI 0.137.0 is out!

🤖 Multi-agent v2 per-thread runtime choice
🌐 Web and image tools in more flows, parallel search
🔌 codex plugin list --json with cached catalog

Changelog: https://t.co/sKpn9YVFmE

[rody @0x_rody]

https://t.co/tIuwDNgyNU

[Matt Van Horn @mvanhorn]

https://t.co/95lFnAyw0e

[Paweł Huryn @PawelHuryn]

https://t.co/y18OojPznE

[rody @0x_rody]

https://t.co/TLFYfXGoBG

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/gvIY96LD7y

[Sydney Runkle @sydneyrunkle]

https://t.co/qV7B67dyxV

[Codez @0xCodez]

https://t.co/RSly7N44jl

[mem0 @mem0ai]

https://t.co/cQ9EO2FSzc

[Avi Chawla @_avichawla]

A harnessed LLM agent, clearly explained!

Most people picture this as a model with tools bolted on. The real architecture inverts that relationship.

The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime.

Three dimensions orbit the harness core:

- 𝗠𝗲𝗺𝗼𝗿𝘆 holds the state a model shouldn't carry in weights or context. Working context, semantic knowledge, episodic experience, and personalized memory each have their own lifecycle.

- 𝗦𝗸𝗶𝗹𝗹𝘀 hold procedural knowledge. This can cover operational procedures, decision heuristics, and normative constraints that specialize the general model per task.

- 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 hold the interaction contracts. Agent-to-user, agent-to-agent, and agent-to-tools are three distinct surfaces with their own failure modes.

Between the core and these modules sit the mediators, like sandboxing, observability, compression, evaluation, approval loops, and sub-agent orchestration.

They govern how the harness reaches out and how state flows back in.

The useful question this framing unlocks is: for any new capability, where should it live?

- Stable knowledge goes to memory
- Learned playbooks go to skills
- Communication contracts go to protocols
- Loop governance goes to the mediators

Harness design becomes a question of what to externalize, and how to mediate it.

I'm building a minimal agent harness from scratch and will open-source it soon.

In the meantime, my co-founder wrote an article about the anatomy of Agent Harness, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent.

Read it below.

[Ruben Hassid @rubenhassid]

https://t.co/0j9ObTybve

[Rahul @sairahul1]

https://t.co/WN0MsLNjDw

[Garry Tan @garrytan]

You should want to control and host your own memory

It’s the one thing that you should be able to take to any platform

Watch for this to be a defining battle in the new browser war: the AI harness wars of 2027

[Codez @0xCodez]

https://t.co/PxgJNWBMWA

[Moysei @0xMoysei]

https://t.co/KEZARh9bRL

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/B6FAzb6Hf8