For anyone spending their weekend patching their homelabs, VPSs, or anything self-hosted
$> tmux new -s copilot-patching
CTRL+b c
New window for each VM / VPS
SSH into your VMs and PVEs in each window
Then ask your agent to use the copilot-patching tmux session to do the upgrades, cleanups, hardening of your setup, fixing your firewall settings.
You're welcome.
$> tmux new -s copilot-patching
CTRL+b c
New window for each VM / VPS
SSH into your VMs and PVEs in each window
Then ask your agent to use the copilot-patching tmux session to do the upgrades, cleanups, hardening of your setup, fixing your firewall settings.
You're welcome.
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We've entered the phase of AI writing code that I can barely understand.
The worst part is that even if I align with the model on a methodology, the agentic harnesses are fully encapsulating the intermediary steps needed to make sure the model & agent stay on track.
I often find myself needing to have elaborate "debriefings" after the fact to force the agent to reveal "how" it went about its work, and for the past several weeks those haven't ended well.
I've been experiencing:
- Misalignment on intentions that were never part of the context, because I'll always have context that is just not written anywhere.
- Assumptions made by the models in the intermediary steps that were never shared with me, but the model assumed they were OK.
- Exaggerating or diminishing information so that the agent can reach its objective and terminate its task; there is definitely some bias towards "finishing" work.
- Elaborate use of tools that I didn't sanction myself. For example, one model decided to load GiBs of data into a local sqlite instance to run some queries on it and remove duplicates.
The people building demoware, prototypes, throwaway apps, self-hosted solutions will most likely not experience these issues. The tasks are most likely simple, isolated, and straightforward.
The engineers running large, mission- or business-critical systems (distributed or otherwise), building on top of years of embedded institutional knowledge, mistakes are not acceptable.
I don't have solutions to these problems. I'm currently experimenting with:
1. Forcing the models to use simple language, not to invent jargon especially that's not provided as context.
2. Forcing the models to maintain an evidence trail especially when they are running data analysis work for me.
3. To do a step-by-step walkthrough of their method and to demonstrate with evidence the outcomes they're presenting to me.
4. To maintain artifacts of the input and output so that I can review on a need basis.
This is a dangerous phase. The cutting edge models are very good at making you "believe they're trustworthy."
I often find them using unnecessarily complicated language, which can evoke in the reader a sense of trust in the outcome. Do not fall for this trap.
Verify.
Reviewing pull requests you don't even understand isn't going to save you.
The worst part is that even if I align with the model on a methodology, the agentic harnesses are fully encapsulating the intermediary steps needed to make sure the model & agent stay on track.
I often find myself needing to have elaborate "debriefings" after the fact to force the agent to reveal "how" it went about its work, and for the past several weeks those haven't ended well.
I've been experiencing:
- Misalignment on intentions that were never part of the context, because I'll always have context that is just not written anywhere.
- Assumptions made by the models in the intermediary steps that were never shared with me, but the model assumed they were OK.
- Exaggerating or diminishing information so that the agent can reach its objective and terminate its task; there is definitely some bias towards "finishing" work.
- Elaborate use of tools that I didn't sanction myself. For example, one model decided to load GiBs of data into a local sqlite instance to run some queries on it and remove duplicates.
The people building demoware, prototypes, throwaway apps, self-hosted solutions will most likely not experience these issues. The tasks are most likely simple, isolated, and straightforward.
The engineers running large, mission- or business-critical systems (distributed or otherwise), building on top of years of embedded institutional knowledge, mistakes are not acceptable.
I don't have solutions to these problems. I'm currently experimenting with:
1. Forcing the models to use simple language, not to invent jargon especially that's not provided as context.
2. Forcing the models to maintain an evidence trail especially when they are running data analysis work for me.
3. To do a step-by-step walkthrough of their method and to demonstrate with evidence the outcomes they're presenting to me.
4. To maintain artifacts of the input and output so that I can review on a need basis.
This is a dangerous phase. The cutting edge models are very good at making you "believe they're trustworthy."
I often find them using unnecessarily complicated language, which can evoke in the reader a sense of trust in the outcome. Do not fall for this trap.
Verify.
Reviewing pull requests you don't even understand isn't going to save you.
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Building GitHub Actions in a weekend?
I always chuckle a little when AI maximalists make that claim. Conceptually it sounds simple, but Actions’ architecture is far from it. This small diagram is a high level illustration of the services, components and databases involved in ting a workflow run and its jobs. This doesn’t even begin to cover the vast landscape needed to manage artifacts, policies, packages and the scale of the hosted runners fleet etc.
200 million jobs per day explode into billions of events, and API calls, thousands of compute cores as well as petabytes of storage.
Tomorrow I’ll be sharing at #CodeEurope26 how we solved a critical availability risk for actions, it is but one of the many work streams everyone is super busy with. No flashy tech, no obscure black box solutions, just old fashioned engineering rigor and world class execution.
If you’re in Warsaw attending the conference, don’t skip this talk and come say Hi.
This session will be recorded and I’ll share it here once it’s available.
I’ll be back to posting more educational tech content soon, but first, we need to finish making sure the CI and Agentic execution platform of the internet is well taken care of.
I always chuckle a little when AI maximalists make that claim. Conceptually it sounds simple, but Actions’ architecture is far from it. This small diagram is a high level illustration of the services, components and databases involved in ting a workflow run and its jobs. This doesn’t even begin to cover the vast landscape needed to manage artifacts, policies, packages and the scale of the hosted runners fleet etc.
200 million jobs per day explode into billions of events, and API calls, thousands of compute cores as well as petabytes of storage.
Tomorrow I’ll be sharing at #CodeEurope26 how we solved a critical availability risk for actions, it is but one of the many work streams everyone is super busy with. No flashy tech, no obscure black box solutions, just old fashioned engineering rigor and world class execution.
If you’re in Warsaw attending the conference, don’t skip this talk and come say Hi.
This session will be recorded and I’ll share it here once it’s available.
I’ll be back to posting more educational tech content soon, but first, we need to finish making sure the CI and Agentic execution platform of the internet is well taken care of.
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People who can simplify complicated concepts so that laymen can understand them, are not the same people as those who can only reason about simple concepts.
At the surface level the output might look identical, but that output was not fabricated by similar machines 😄
The internet hides the machines well
At the surface level the output might look identical, but that output was not fabricated by similar machines 😄
The internet hides the machines well
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The usage we’re seeing is unprecedented, the scale is unprecedented, and the resource crunch is real and not just in engineering talent.
Insane amount of work is going towards stabilising and securing the platform. Actions for example isn’t only handling the growth of CI use, it’s also the platform for all agentic workloads. The strain on our dependencies is real.
It’s a multi-dimensional whammy that no one saw coming at this speed and scale. We’re working hard on it :)
In Actions we also spent a couple of years investing in a major replatforming work to help us get to 10x scale. We hit 100x that scale in the same period. We have to cope with the growth, defend from abuse, fight the malicious actors, build new features, and keep everyone sane.
Everyone and everything is firing on all cylinders!
Insane amount of work is going towards stabilising and securing the platform. Actions for example isn’t only handling the growth of CI use, it’s also the platform for all agentic workloads. The strain on our dependencies is real.
It’s a multi-dimensional whammy that no one saw coming at this speed and scale. We’re working hard on it :)
In Actions we also spent a couple of years investing in a major replatforming work to help us get to 10x scale. We hit 100x that scale in the same period. We have to cope with the growth, defend from abuse, fight the malicious actors, build new features, and keep everyone sane.
Everyone and everything is firing on all cylinders!
OPINION: Over the past year or so, GitHub has received so much hate for reliability, security, and performance issues. We have often been critics ourselves. However it's important to remember that GitHub has graciously provided free version control, CI/CD, and issue tracking for f of that being after the Microsoft acquisition. We shouldnt be so quick to turn on the GitHub teams as they have given us so much. If anything, we should direct criticism at Microsoft's executive team to prioritize more engineering resources to GitHub.
And to be fair, the teams have been delivering. Since the disaster run in early 2026, reliability has been noticeably better over the past few months. At SemiAnalysis, we've worked closely with our GitHub FDE @aus10stone and the GitHub Actions engineering team @nebuk89 & Bassem Dghaidi. They've been incredibly attentive, and if these folks are any indication, the old GitHub engineering culture is alive and well.
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The one man leading the resistance 🫡
Rich people who were too stupid to code before are now superstars. And actually brilliant engineers are made to feel stupid and redundant. Meanwhile coding has been synthesized into beige gooey calorie dense bars made from cockroaches and engineers have to line up with a small plate begging for their share from the token barons who, fortunately, at any given moment can feel generous enough to press a button that makes the tokens fly out like projectile vomit. Engineers at these companies, who sit mere inches from the spigot, frequently bless us with thinkpieces that we too should be doing what they do, and it’s actually quite unfashionable not to do so. Of course none of this is so much engineering advice as it is financial advice.
But sure, “wHy dO pEoPle hATe Ai?”
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Noo…. who would have thought…
🚨 Shocking: Frontier LLMs score 85-95% on standard coding benchmarks. We gave them equivalent problems in languages they couldn't have memorized. They collapsed to 0-11%.
Presenting EsoLang-Bench.
Accepted to the Logical Reasoning and ICBINB workshops at ICLR 2026 🧵
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Clawdbot is fascinating! Took me a few hours to get it setup on an isolated VM on an untrusted VLAN, with a shared folder from my NAS for memory, all running in my small homelab.
It even setup whisper to transcribe my voice notes and respond to my voice prompts!
If this thing vidia’s personaplex for full-duplex speed to speed conversations?
Game over!
It even setup whisper to transcribe my voice notes and respond to my voice prompts!
If this thing vidia’s personaplex for full-duplex speed to speed conversations?
Game over!
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Why is this regurgitation of what's already open sourced in abundance so impressive?
I would be more impressed if this was solving an entirely new problem by remixing existing knowledge. Recreating a utility using what it was trained on seems like a massive waste of cycles.
I would be more impressed if this was solving an entirely new problem by remixing existing knowledge. Recreating a utility using what it was trained on seems like a massive waste of cycles.
Watch Cursor build a 3M+ line browser in a week
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Writing code has never ever been the bottleneck. If I unleash 10 agents running in parallel 24/7 solving well defined problems, AND deploying changes, handling operational problems, troubleshooting alerts, mitigating incidents, I would still have a backlog that takes years to get through and define well enough for an agent to take a shot at
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link- retweeted
constant refactoring is not a sign of perfection, it’s a sign of weak engineering.
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Culture is everything you do not because it serves a KPI, or boosts “morale” or implements an arbitrary leadership paper. It’s everything you do because why the fuck not
Full-scale Oval Office replica inside GitHub's HQ, circa 2015
I don’t think most of you genuinely realize how much verve has been stripped from 2010s startup culture
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I was under the impression that Boston Dynamics was owned by Alphabet, looks like it was sold to SoftBank and then Hyundai!
If this actually goes to market, it’s a big loss for Alphabet
If this actually goes to market, it’s a big loss for Alphabet
NEWS: Boston Dynamics has just released a new video of its upgraded next-generation humanoid robot called Atlas.
• 4 hour battery. Self-swappable for continuous operation
• 6 feet 2 inches tall
• Weight: 198 lbs
• 56 total degrees of freedom
• Now fully electric, ditching • New lightweight mix of aluminum and titanium components
• 110 lbs weight capacity (66 lbs sustained)
• Can reach up to 7.5 ft
• Constantly evaluates its surroundings and adjusts its posture, balance, and grip in real time
• Hands that can reconfigure as needed. Tactile sensors feed data back into the system, helping apply the right amount of force
• Brain is powered by Nvidia chips
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