
Conservative Theology, Liberal Politics, Terrible Puns. Senior Engineer with Hope Media Group. My many opinions are my own. #ActuallyAutistic
Nashville, TN, USA, Earth, Sol
Joined October 2008
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Replying to @BuckyIsotope
Side by side, the wharfs drifted to the event horizon.
Two pier into the abyss.
Forever a pair of docks.
- Old Klingon Proverb
Christopher Coleman (@[email protected]) retweeted
except me, I know exactly what's going on: your backup strategy is insufficient, your primary drive is currently failing silently, and you're about to lose all of your data
Christopher Coleman (@[email protected]) retweeted
It's such a strange time to be a programmer. In a two-minute span it's "DDD and domain modeling generally are more important than ever" then "reading code is a waste of time, just let the agents manage it" then "classical software design principles are what matter." Please help.
Christopher Coleman (@[email protected]) retweeted
Claude writes about 90% of Claude Code's code.
From June to September, merged PRs more than doubled while the share reverted fell by more than 40%.
We wrote about how we changed code review to get there, and how to adopt it:
claude.dev/blog/solving-code…
Christopher Coleman (@[email protected]) retweeted
Random advice: Turning things off really does fix a lot of stuff, but you're not _leaving_ it off long enough. You've got to wait for the capacitors to drain. Just leave it off for an hour. I've fixed washer and HVAC same way.
Christopher Coleman (@[email protected]) retweeted
"It is amazing what you can accomplish if you do not care who gets the credit."
Christopher Coleman (@[email protected]) retweeted
Three useful ways to say no, from Judd Antin:
• Yes No: yes to the need, no to this ask.
• Material No: yes, with the resources.
• Priority No: yes, if something else drops.
A good no makes the real need, resources and trade-offs clearer.
Christopher Coleman (@[email protected]) retweeted
Maybe you're viscerally repelled by a nine-argument function, or a loop with a stack that could be a simple recursive function, or a deep class hierarchy that obscures rather than clarifies control flow. You don't have to think; the judgement just happens. That's fast and useful.
Christopher Coleman (@[email protected]) retweeted
Maybe some people see "aesthetic judgement" and think "how the text looks", as in formatting. And, sure, code can have ugly formatting. But (I assume) every experienced programmer has some aesthetic preferences for structure, not just the arrangement of the pixels on the screen.
Christopher Coleman (@[email protected]) retweeted
"Some programmers have aesthetic preferences about code and they need to get over it because aesthetics don't matter."
Yes, sometimes they don't matter. But aesthetic judgements are also fast. If they're calibrated to match useful properties of code, that's useful.
Christopher Coleman (@[email protected]) retweeted
"AI can replace artists" —person whose walls are empty other than a superhero movie poster
"AI can replace musicians" —person who never goes to live performances
"AI can replace authors" —person who only reads Twitter
Let’s go! Has been fun to try this out.
TanStack Start 1.0 is officially stable! 🎉
A full-stack web framework for React and Solid, already used to build millions of apps, from Fortune 500 companies to your favorite tools and weekend projects.
tanstack.com/start
Christopher Coleman (@[email protected]) retweeted
$100M across 500+ addresses so far. Crypto hardware wallet owners are freaking out because of a new supply chain attack. Let’s see how we could make it worse!
People are opening their hardware wallets to look for hardware implants. REMINDER: 5 years ago I converted a Ledger into a malicious wallet WITH NO VISIBLE CHANGES. So visual inspection isn’t everything.
The attack seen today is a hardware backdoor that reads the seed phrase right off the screen & sends it to the attacker using its own cellular radio. And it seems the attacker may have purchased an official Ledger reseller so they could ship backdoored wallets to customers. (It’s all still unfolding, not everything is confirmed, new implants are showing up, etc)
A Chinese company bought an official reseller of Ledger, & possibly used an NDA to prevent it from being made public. I will say that even if the new owner wasn’t complicit, mergers & acquisitions are very attractive times to target a company. Lots of stuff is changing quickly, lots of new names, lots of urgent needs, etc.
In theory, the reseller is where they inserted the implants. They opened legit Ledger wallets and added an extra PCB with extra components. The implant tapped the lines of the Ledger’s screen & was able to read the seed phrase exactly as shown to the user. This phrase was then sent out over a slow cellular radio designed for low traffic IOT.
Tapping the screen has been a known attack. Joe Grand did a talk on it earlier this year. We have seen more “primitive” looking implants that do it.
But buying a company is a big step forward from prior attempts. I wouldn’t be surprised if there was some state funding here. I guess we will find out as people dig into the corporate paper trail of the business, the account associated with the cell service, etc.
Hardware wise, they cleaned things up a lot to make it less noticeable to a wallet owner who opens the case. But I can’t help but wonder why they didn’t improve a few areas… I’ll go into that in replies to this post.
🧵1
Christopher Coleman (@[email protected]) retweeted
Some mistakes you might be making:
Your effort level is too high
Your skills are too long
You're not aligning before you start work
You're aligning too often, and too hard
Your automated checks are too lax
Your custom tooling is too token-inefficient
You aren't doing enough automated review
You aren't reviewing previous coding sessions (with an agent)
You're doing too much human review
You aren't vibe coding enough prototypes
Your agent can't verify its own work
You're babysitting the agent too much
You've overloaded your global steering files
Your codebase is hard to navigate
You aren't building a shared language
Your codebase architecture allows bad tests to be written
You aren't thinking strategically enough
You aren't thinking enough
Christopher Coleman (@[email protected]) retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Christopher Coleman (@[email protected]) retweeted
Have a great Thanksgiving, Canada.
One of the most ancient of all celebrations, of harvest time. Share it with others if you can. Enjoy the weekend!
Christopher Coleman (@[email protected]) retweeted
This is really excellent stuff, and way better than it needs to be
Worth a watch
Replying to @mattpocockuk
@mattpocockuk's skills repo has more stars than React, and the most installed skill is one line long.
I animated how his whole workflow works, from /grill-me to /retro
Christopher Coleman (@[email protected]) retweeted
This is about to go crazy for vibe coders
Introducing e2e
The agentic testing framework for any app
~/ npx e2e init
→ Fully Open Source
→ Mix deterministic and agentic APIs
→ Supports web, mobile, and more
→ Bring your own agent and infrastructure
→ Run locally or in CI
tester.army/e2e
Christopher Coleman (@[email protected]) retweeted
Replying to @mattpocockuk
@mattpocockuk's skills repo has more stars than React, and the most installed skill is one line long.
I animated how his whole workflow works, from /grill-me to /retro
Christopher Coleman (@[email protected]) retweeted
Prompting problem more than model selection (every model will regress to the mean, that’s just what they are). “Generate a list of design axes + values for this build, write a script to randomly select 5 permutations, and have subagents build each experiment”.
























