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HEY @X, help me find my people.
I'm building with AI every day: Claude orchestrating, GPT reviewing every change, local models on a 118GB AMD lab, Omarchy, Ubuntu, and Windows.
Into agents, local models or Linux? Say hi. I follow back real builders.
6 session running today, 5 on my dataset pipeline and machine verification, 1 on the cyberdeck PCB's. I have to keep the wheels turning, feeling tired, Boss.
How do I get so much usage and maintain such high cache hits?
How caching works: each time I answer, the whole conversation so far is sent again: the system instructions, the tool list, CLAUDE.md,
MEMORY.md, then every message and tool result. Anything identical to the last request from the start onward is read from cache. Only the
new part at the end is fresh.
Why ours is so high:
1. Long-lived sessions. This session and the workers have been running for days. The history grows to hundreds of thousands of tokens,
and each turn adds only a few thousand. So almost everything sent is a repeat.
2. A big, stable start. CLAUDE.md, MEMORY.md (about 21 KB), the tool definitions, and the skill and agent lists are the same every turn.
Edits to MEMORY.md mid-session don't change what's already loaded, so they don't break the cache.
3. Small turns. Short messages, compact agent reports and dispatches that pass file paths keep the new part at the end small compared to
the cached history.
4. The one-hour cache window. Session notifications and background agents call back well inside an hour, so the cache rarely expires
between turns.
5. Subagents reuse fixed definitions. Each agent type starts from the same definition file, so repeated runs of the same agent share a
cached start.
What lowers it:
- Compaction: the history gets summarized, so the next request starts fresh once.
- Gaps of over an hour, like the reboot coming up.
- Switching models.
- Brand-new sessions: their first turns are all fresh.
Hi Boris,
First, what works. We run one coordinating Claude Code session plus four worker sessions in parallel on one Windows 11 machine. They message each other, background agents run overnight, and task notifications bring results back without anyone polling. That has carried real work for days.
Here is the friction, each with what we measured and what would fix it.
**1. Hung status-line commands are never cleaned up.**
Our status line is a small bash script that reads the JSON Claude Code sends on stdin. On Windows those runs piled up instead of exiting. We counted 162 orphaned bash.exe processes (parent gone) running the status-line script, 41 orphaned timeout.exe processes, and later about 24 orphaned `py.exe -c "import sys, json ... json.load(sys.stdin)"` processes from the same script, all waiting on stdin. Starting Python took 18 seconds until we killed them. Afterwards it took 0.1 seconds.
To reproduce: on Windows with Git Bash, set a status line that reads stdin to EOF, keep a few sessions open for a day, then count processes whose command line contains the script name.
Ask: give the status-line command a hard timeout, close its stdin once the JSON is written, and kill the process tree if it overruns.
**2. Every hook is a shell, and shells are expensive on Windows.**
Each PreToolUse hook runs as `bash -c ...` through Git Bash. Under load, one Git Bash startup cost 4 to 7 seconds. A `bash -c` that ran `true` three times took 26.7 seconds, while Python started in 0.13 seconds. We run a few hooks per tool call, so simple reads became slow. In the end only a reboot brought the machine back.
Ask: on Windows, run hooks without a shell (exec the interpreter directly), or keep one hook process alive and send it each event.
**3. Simple shell tool calls time out.**
On the same machine, read-only commands like `cat`, `grep` and `git status` regularly hit the 120-second tool timeout and moved to the background. We think it is the same shell start-up cost as point 2, but we have not proven it.
Ask: show how long the shell took to start, separately from the command, so this is visible.
**4. Windows resolves the wrong interpreter quietly.**
Inside the bash the harness starts, `py` resolved to the Microsoft Store alias stub (`WindowsApps\py.exe`), not the real launcher. That stub hangs when fed a pipe, which is part of point 1. From PowerShell, `bash` resolved to the WSL stub (`WindowsApps\bash.exe`), so a test run silently ran hooks under WSL and printed "install a distro" text instead of failing loudly.
Ask: report which `bash`, `py` and `python` a shell resolves to at session start, and prefer the Git Bash and real Python paths over the WindowsApps stubs.
**5. Permission refusals with no narrow pre-approval.**
Auto mode refused actions I had already approved for this project: syncing our own code to our own test machine, and stopping processes a session had started itself. Each session decides on its own, which is correct, but the work then stops until I step in.
Ask: a per-project allowlist for one specific action category, for example "sync to host X", that every session in the project honours.
Thanks for building something we use all day.
Claude Opus 5.5
This is the unit I will be using in My cyberdeck build, my only wish would have been 32GB RAM. I think It will work out fine for my use case.
How well can LattePanda Mu Ultra handle local AI?
We tested the Intel Core Ultra 5 226V with Qwen, Phi, and Gemma models using OpenVINO and INT4 quantization.
From fast prompt processing to local multimodal inference, see the results: lattepanda.com/blog-323376.h…
Anthropic security chief is talking.
A beast of a tiny PC, I love it.
One board. More GPUs. More AI possibilities.
Run local LLMs, explore multi-model workflows, and scale up with OCuLink.
Meet LattePanda Sigma 2.
🛒:dfrobot.com/product-3164.htm…
Anyone who ever thought about building hardware, start now, these tools with SI running them is a awesome experience.
WholeBoard now sims the board inside its enclosure. ESP32-C3 board, 333 mW: 42.6 °C in open air, 52.0 °C sealed in a PLA box. Add lid and side slots and it drops to 50.4 °C. Still in spec in a 40 °C room. Sealed or vented for yours?
github.com/DingoOz/WholeBoar…
We built a tool to merge finished work safely.
Its first real job? It refused. A stray file from October 6 was sitting in the folder.
It was right.
A guard that blocks you for a good reason is the guard working.
I ran the rules for how we build training data past three reviewers: an AI reviewer, an outside reviewer and a second model.
Each caught mistakes the others missed. One reviewer's suggested fix would have caused a new bug.
One reviewer isn't enough.
For one feature we generated 326 deliberately broken versions. Each had one setting swapped or ignored.
Then we checked that every single one got caught.
You don't trust a test until you've watched it fail.
Interesting read, and explains a-lot about how they find bugs so much better.
Anthropic's latest research on formal verification is pretty fascinating, and I will try to explain that in simple words.
Normally, when developers test software, they throw millions of different inputs at it to see if something breaks in the system. But even after a million successful tests, there's always a chance the next input could crash the program.
That's where formal verification comes in.
The idea is pretty simple. What if you could use mathematics to prove that your code won't crash, for every possible input within certain limits?
We've been able to do this for decades, but writing those mathematical proofs takes an insane amount of time and expertise.
So Anthropic got Claude to do the heavy lifting.
Claude looked at the actual compiled code and worked out the mathematical proofs, then it passed them to another tool called Rocq to check whether they were correct.
And while doing all this, it discovered bugs that had gone unnoticed despite years of testing.
The researchers ended up generating 180 MILLION lines of proofs across 123 open-source libraries, covering 239 test entry points.
What's really interesting to me is that we don't have to blindly trust whatever Claude generates here. The mathematical proof gets checked independently.
Of course, proving that certain parts of a program can't crash under specific conditions doesn't mean the entire program is bug-free.
But imagine being able to run mathematical proofs on your code as easily as you run unit tests today.
I think that could change software engineering quite a bit.
How did I miss this, it’s pretty wild.
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10.
Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years.
Now, we’re building a fusion reactor in San Francisco to achieve record power density.
More fake nerfed posts with no real data and a image prompt. Why, when will it end? Do these people even know how LLM's and testing them works? No, no they don't. What happened to credibility?
unavailable
Anthropic keeps winning day in and day out, and I love it!
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:
Google, will they ever build something i even want to try? Well they haven't yet.
Starting from a single Nano Banana image, Gemini 4 Argon created this finished 3D cathedral audio-reactive scene.
Driving Blender MCP in an autonomous loop in @Antigravity to:
🏛️ Sculpt intricate Gothic architecture
🎥 Align complex camera grids
🔊 Stage dynamic, audio-reactive lighting
Open ai is making the play to be the fast food of the SI industry. It is what it is.
ChatGPT has brought in Messi as an ambassador.
Its official OpenAI is giving up on power users and pivoting to the novice user.
I don’t see Anthropic paying professional athletes to shill their products but rather they let their models do the talking.
OpenAI has Messi, Claude has Opus 5.5
Do you prefer Messi or Opus 5.5?
Doing great things for local models.
AgentionAI models have passed 1.1M downloads on Hugging Face. 🎉
Qwen3.8-27B AP quants at 650k+, and Gyro, my rotor quant for running Qwen3.8 Flash Next (126B) on a 32 GB card, is past 170k in under two weeks, with Strata support live.
Thank you to everyone who tested, reported bugs, shared results and supported me. Special thanks to @coldniko, who gave the first donation to the GPU fund and added Strata support.
Link in bio if you’d like to help with the next big one.













