Operating Partner. Leading AI & Tech @ Growth Factors (Bregal PE, 30+ companies). Building Flow-Next, GNO, Dettivo & DocIQ. Agentic software factories. Dad x2
Basel, Switzerland
Joined June 2008
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If you want the best possible local dictation and meeting transcription with full agent and theming support on @OmarchyLinux then I have something for you:
github.com/gmickel/dettivo-l… (the app)
github.com/gmickel/omarchy-d… (plugin)
Gordon Mickel retweeted
So bullish on MCP. Its utility exceeds the "M" part: even AI aside, it'll be a civilization-scale accomplishment to get all our systems communicating through one universal open protocol.
A missing piece was how to get arbitrary callbacks across sessions. MCP could read and write, but it couldn't tell you later "this event happened." MCP Events fixes this.
We're adding support in our own MCP. Say an agent makes a deployment, and wants to know when it's complete. Instead of polling, it can subscribe to 𝚍𝚎𝚙𝚕𝚘𝚢𝚖𝚎𝚗𝚝.𝚜𝚞𝚌𝚌𝚎𝚎𝚍𝚎𝚍.
PSA: With Vercel Queues and Workflows, writing the backend machinery for your own MCPs to do this is easy peasy.
Vercel MCP now publishes events.
Agents can subscribe to events like 𝚍𝚎𝚙𝚕𝚘𝚢𝚖𝚎𝚗𝚝.𝚜𝚞𝚌𝚌𝚎𝚎𝚍𝚎𝚍 and 𝚍𝚎𝚙𝚕𝚘𝚢𝚖𝚎𝚗𝚝.𝚎𝚛𝚛𝚘𝚛 and get webhook notifications when they fire. No more polling for changes.
Changelog → vercel.com/changelog/vercel-…
Gordon Mickel retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
Was at an agentic development event in Switzerland a couple of months back and a guy from Proton was there. Very anti-agentic dev, so much so that I wasn't entirely sure why he was there
It's very important for leaders to understand that once you're dealing with a "rebellion" there's nothing you can do that'll appease the rebels. It'll never be enough. Not for them, not for the press egging them on. They will not rest until your head is on a spike.
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Gordon Mickel retweeted
ChatGPT in Germany in 2027
Yesterday France announced tax of €0.055 per GB on internet traffic
Today Germany is considering taxing all AI-generated revenue
You will see European countries keep clawing more and more money from foreign companies as they are slowly running out of money themselves to pay for their pensions and welfare state
Add to this the planned wealth taxes and exit taxes on any European who will try to leave (already active in many EU countries)
Europe is running out of money
Readers added context they thought people might want to know
The post inaccurately claims France "announced" a data tax; it was a narrow 11-10 vote on a budget amendment in a near-empty chamber, opposed by the government and far from law. German AI tax ideas remain unadopted ministerial discussions without formal proposals.
stephanelarue.com/taxe-au-gigaoc…
tagesschau.de/inland/ministe…
davemanuel.com/2026/10/30/wha…
Gordon Mickel retweeted
Every time someone says "ai can't write good code", I know that they can't write good code.
It's that time again, you know, when you have to reauth your Claude accounts for absolutely no reason
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Immediately scrolled down to see to find where it says that it won't be available in Switzerland, wasn't disappointed.
Apple Homeview is official. $299
Going hands-on today, so ask me anything here!
apple.com/newsroom/2026/10/m…
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Gordon Mickel retweeted
Yeah, people really deeply, deeply hate (as in HATE) AI because it's projected with these fear narratives instead of "You can do amazing stuff. Show me the amazing stuff you have built."
Those who reject AI instead of leaning into AI are going to find themselves unemployable. It concerns me deeply. It's a form of self-sabotage and self-harm.
Gordon Mickel retweeted
the slop conversation is so one sided, it's not just that you can produce more code
you can also observe, debug, and refactor more code
even if the code is slop you have such good tools to deal with it. even if you write code manually, it's still 100x easier to maintain it
Gordon Mickel retweeted
Day 7/
We have increased default context length for GPT-6.1 Sol to 500k when used through Codex & Work, up from 272k or an ~ 80% increase.
We will go higher in the future, but it helps reduce occurrence of compactions and with tasks that require keeping a lot of context and nuance over periods of time. It was carefully tested and is the new sweet spot for most of our users. You can still override it if you want to be higher our lower, with maximum context length of GPT-6.1 Sol of 1M.
This, 100%. And another lukewarm take/bugbear:
I still run into so much resistance doing 'agentic R&D transformations' at companies. Lots of reasons, but the one that always gets me is this weird belief that everyone was somehow writing amazingly high-quality code before AI came around.
Been doing this for 25 years and I can count the number of great engineers I've worked with on my hands and feet.
codebases have always contained slop. we have always been okay with this because code rarely needs to be perfect, outside of mission critical "hard" (eg embedded) software.
swe is a unique form of engineering because you're often building something that constantly evolves and is almost organic - user tastes change, your mental models evolve, and features come and go.
admittedly, i and many others who've been coding for a long time were initially (like back in 2024) skeptical that agents would write production code. "but what about all the slop that would enter our beautiful codebase?".
well, even before, we had human slop, and there was a lot of it! if we're being truthful, most people call "code i didn't write" slop. the reality is that 99% of people (i include myself in that number) don't even know what Good Code looks like, let alone be capable of writing it. i would propose that Good Code is:
• correct
• bug free
• fast and cost efficient to run
it has always been exceedingly difficult and expensive to write truly Good Code, because for the longest time (maybe @bendlang will change that), code was not something you could cheaply and formally guarantee to have all 3 of these properties. this is why writing hard software is much more difficult and expensive, because depending on the use case, you may only get one chance and so you have to be right from the start.
but the vast majority of software we use does not need to be Good Code to provide value to users. in fact, it is very likely that most software we use is not formally verifiable! we accepted some bugs in exchange for velocity and progress, so we rely on tests and o11y instead and have adopted a more incremental approach. this is a good thing!
agents help us move faster, and if you're open to the idea, are capable of helping us write better code. it won't be Good Code (that is still expensive), but it is likely to be much better on average at scale.
the new craft of swe is to figure out how to do this. it's your job now! if your productivity with and without agents is not significantly different, you're doing something wrong. but it's not too late to embrace it and use it as an opportunity to learn a whole new skill tree
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Gordon Mickel 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.
Gordon Mickel retweeted
A big % of tech people don't get it
Normies won't ask "build me software"
They will ask "I have this photo and I want to manually edit it" and it will spin up a Photoshop clone
They will ask "I need to know if sales are going up or down this quarter from all different ecommerce platforms we use" and it will spin up an analytics dashboard connecting all APIs with scheduled crons that keeps it up to date forever
They already do!
They won't know they're building software because their AI app/agent will do it for them
And then they'll stop using most regular software!
I'm sorry, but your definition of "normies" is some tech-adjacent person.
A regular person has absolutely no fucking idea what you're talking about here, and frankly, the younger generation is as dumb with tech as the older one, maybe even worse.
The average teen has a hard time using a mouse and a keyboard, and you're talking about building their own software with AI, where a fraction of population worldwide knows what AI is
Gordon Mickel retweeted
I can't emphasize enough how important this @a16z chart seems and how few have internalized it.
Eagerly waiting for the day where I don't need to reauth a bunch of agents/connectors/mcp servers etc.
Every damn time.
How is this not solved
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This right here
🗞️ if your team is too busy doing their 'normal job' to experiment with AI, you're preparing them to be replaced
ghuntley.com/replaced/
In this post, I guess this's the last time I'll say it. AI use is no longer optional if you wish to be employed. What follows is advice for current employees and hiring managers on how to hire in 2026 to source AI-first candidates.
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