Controls FreeCAD from Claude Desktop using the Model Context Protocol

https://github.com/neka-nat/freecad-mcp https://x.com/tom_doerr/status/2085056404969127975

Runs a five-parallel-agent AI sales team inside Claude Code to research companies, qualify leads using BANT and MEDDIC frameworks, map buying committees, generate personalized outreach, and produce professional PDF pipeline reports.

https://github.com/zubair-trabzada/ai-sales-team-claude https://x.com/tom_doerr/status/2085245581782380630

Builds a Wi-Fi controlled DIY drone using ESP32 and MPU6050. Features height and position hold capabilities through LiteWing, a structured development platform.

https://github.com/Circuit-Digest/ESP-Drone https://x.com/tom_doerr/status/2085314067778994596

L0p4Map is a professional-grade network monitoring and visualization tool combining high-speed ARP discovery with nmap integration. Built for security researchers and network admins, it offers a clean, modern dark UI for real-time infrastructure visibility across Linux, Windows, and macOS.

https://github.com/HaxL0p4/L0p4Map

A Chinese-language, Linux-focused guide that walks beginners from environment setup to full and efficient fine-tuning of 50+ open-source LLMs (LLaMA, ChatGLM, InternLM, etc.).

Explore it here:
https://www.opensourceprojects.dev/post/self-llm https://x.com/GithubProjects/status/2084950051210854705

a fully open-source self-improving harness.

Prime Intellect just shipped Prime Agent, which turns a frontier model into a Recursive Language Model.

context becomes a variable the model programs over, and sub-agents become ordinary function calls.

let's understand what all of this means:

almost every agent you use today works inside fixed harness. someone wrote the system prompt, picked the tool schemas, decided when history gets compacted, and shipped it.

the model spends part of every session working around that scaffolding instead of working on the task. when the same failure repeats three times in a long run, you are the one who fixes it, after the run is over.

Prime Agent removes that ceiling with two pieces.

the first is the RLM design. a persistent IPython kernel is the model's only tool, so long inputs never have to enter the prompt at all. the model greps, partitions, and spawns child calls over the data instead of reading all of it back in.

the second is the Continual Harness. four kinds of state sit outside the conversation and stay writable:

→ Prompt: supplemental instructions the agent appends when it learns something the base prompt never told it.

→ Memory: findings from earlier turns that would otherwise die with the context window.

→ Skills: recurring workflows packaged as importable Python, so the next run imports the procedure instead of rediscovering it.

→ Sub-agents: specs for the children it spawns, tuned once and reused across parallel, background, and long-lived runs.

the /refine command reads the current trajectory, applies the smallest edit it can justify, and records what triggered it. the base system prompt stays immutable, and any update can be rolled back by ID.

with Opus 5 driving it, Prime Agent reports 95.5% on ARC-AGI-3, just past the reported human expert baseline of 95.4%. when that benchmark launched, frontier models were scoring under one percent. nobody trained a new model to close that gap.

worth noting that in Factorio runs the same loop found and then optimized scoring exploits, which is roughly what you would expect once an agent can edit its own instructions.

with Opus 5 driving it, Prime Agent reports 95.5% on ARC-AGI-3, just past the reported human expert baseline of 95.4%. when that benchmark launched, frontier models were scoring under one percent. nobody trained a new model to close that gap.

the scaffold stopped being something you configure once and became something the run improves as it goes.

Prime agent is MIT licensed, single command install, works with open and closed models.

check it out on GitHub: https://github.com/PrimeIntellect-ai/prime-agent

since we are talking about RLMs, i wrote a detailed article on how Recursive Language Models work.

the article is quoted below.

https://roadmap.sh/ offers community-driven, interactive roadmaps that let you click through nodes to explore topics across dozens of developer paths.

- Interactive nodes with links to detailed articles on each topic
- Roadmaps for frontend, backend, DevOps, AI, data science, and 40+ more paths
- Beginner-specific versions for key tracks like frontend and Git/GitHub
- Includes best practices, questions, and a YouTube channel for additional learning

Call.md records meetings and performs real-time dual-channel transcription. It provides live assistance during calls and can automatically trigger external tools using the Model Context Protocol.

https://github.com/video-db/call.md https://x.com/tom_doerr/status/2085416710702526804

I built a GitHub repo to help you learn system design & AI engineering.
It gives you:

• System design foundations
• AI engineering fundamentals
• Top System design interview questions
• Deep dives into real-world architectures
• Simplified engineering case studies with visuals

PS - Star and/or save this repository now if you find it valuable:

https://github.com/systemdesign42/system-design-academy

Controls smart home devices with overlays for Android TV

https://github.com/Trooped/QuickBars https://x.com/tom_doerr/status/2084748365061837118