I work at Fin.

Swords, Dublin, Ireland
Joined May 2009
I love a great low level performance write up.
In our Sysbench tests, switching WAL commits from fsync to fdatasync and prewriting zero-filled journal regions improved Dolt’s write performance by 10–40%. Aaron explains why the change works, where it doesn’t, and some of the tradeoffs: dolthub.com/blog/2026-10-13-…
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This is perfect.
everyone is asking us at @cxodev about calculating ROI on AI, here's what we're saying: 1. focus on engineering, it's 80%+ of your spend - if you're looking to improve cost efficiency its the big lever to move (so stop wasting time in other depts) 2 . model tests + evals are your friend. learn to run them for the most expensive parts of your business 3. spend time with your top spenders: they're either a) doing things your full team should do or b) an easy spot to save some $$$ 4. you're probably underspending relative to oppty all things considered 5. PR velocity is a good not great measure of eng ROI. you need to tie to actual product intiatives and use 6. if you're not sucking up session data + traces you can't really tell if your team is using AI on the right things 7. everyone is blowing through their 12 month budget in 4 months. you're not alone 8. aim for 2-10x improvements in ops efficiency. 30% is a rounding error. 9. time saved =/= ROI. what are you doing with that time? 10. you should be eliminating whole categories of human toil 11. nobody calculated the roi of email. some of this is simple work transformation 12. idk we still like tokenmaxxing 13. you will spike up and then it will be possible to pull down costs (but not output) - we see it all the time. intelligence is getting cheaper. 14. revenue per fully loaded employee is emerging as the north star guide. most leaders won't say that out loud to their teams. 15. spend capping your top users is dumb. they're the ones most likely to be highest impact if you really look. 16. sorry, but you just don't have that many fable-level problems. stop making it your defaut. what am i missing?
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Brian Scanlan retweeted
gist.github.com/minuteman3/a… give it a try - you'll need MySQL 8.4 and your mysqld will probably need --max-heap-table-size=1G --tmp-table-size=1G, but then: mysql -e 'CREATE DATABASE doom' mysql -N -r doom < doom-play.sql
Inspired by @QuinnyPig running Doom entirely in DNS and regretting that I'll miss his visit to the Fin office tomorrow, I made a program to turn a static binary compiled for RISC-V into a self contained mysql dump which runs entirely in one recursive CTE. Doom runs at 1.5fps!
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Brian Scanlan retweeted
Inspired by @QuinnyPig running Doom entirely in DNS and regretting that I'll miss his visit to the Fin office tomorrow, I made a program to turn a static binary compiled for RISC-V into a self contained mysql dump which runs entirely in one recursive CTE. Doom runs at 1.5fps!
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This is the most insulted I've ever been on Twitter in ~20 years, and that's saying something. Not to pull receipts, but I was the first multi-pint drinker on Cheeky Pint, and we spoke in detail about various beers on the episode itself.
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Brian Scanlan retweeted
I'm thrilled that @brian_scanlan from @fin_ai will be joining my AI Transformation course as our first guest speaker. Fin has transformed itself into one of the most AI-pilled organizations out there. They are now reaping the rewards of this transformation, enjoying a 3x increase in their engineering velocity, while so many teams remain stuck at only a 30% improvement. So much of their success comes down to the work they've done to build a robust AI platform that compound's the team's effectiveness, including: - Standardizing and betting heavily on a single agentic platform for the entire team - Creating a shared skills marketplace that anyone can publish their skills to and enable anyone on the team to re-use - Creating the systems so that designers can ship code to production, significantly increasing the product's UI polish I can't wait for Brian to share Fin's playbook with the class.
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Brian Scanlan retweeted
Good time to remind people on the Internet to read this akshay.co/posts/compactions-…
confirmed: you probably shouldn’t use claude code’s full 1m context window. two reasons: - compacting a massive context is expensive and burns through your token quota. - output quality gradually degrades as context fills up. the model starts missing details and losing track of earlier instructions. 400k tokens seems to be the sweet spot between cost and output quality. you can configure claude code to automatically compact at 400k by adding this to ~/.claude/settings.json: { "autoCompactWindow": 400000 } 400k isn’t a magic number, but there’s little point in filling up 1m tokens just because you can 😁
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Brian Scanlan retweeted
volume does matter look, i get it. if you were an engineer before agents came along, you're rightfully skeptical about lines of code and number of PRs being used as a metric for any kind of productivity. and i agree! or at least, i used to. for ease of explanation i will talk about this from the perspective as being an engineer on a large team of at least 50. volume didn't matter before because we focused on impact. you could have high impact even with a few PRs: for example you could make a one line config change and save the company millions of dollars. but before agents, the times where i saw volume come into the conversation were for the outliers. on the negative end you might have someone who is struggling to perform, on the other end you might have someone who was a "coding machine" archetype. a coding machine was someone who could just lock themselves in a room and emerge with a stack of PRs that solve a huge number of problems. so volume did matter, but only at the tail ends of the distribution, and combined with impact. if you've ever worked at a large tech company before, you'll get what i mean. the thing about agents is that big tech co problems are now small-medium co problems as well. how do agents change this calculus? well, everyone now has the ability to become a coding machine. you have incredibly capable frontier models that can write code better than any of us, at a rate much faster than we can. in this world, if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive? alien intelligence is here, and yet you can't outship a human coding machine? this brings me back to why i keep talking about trust (xitter.cf/poteto/status/21020504…). if you haven't put in the work to trust your agent's output, it is very difficult to scale up your productivity. and yes, this approach does require more tokens, but i think about this in terms of cost per intelligence. before agents, this cost was very high - you needed to hire many software engineers with high 6 figure salaries to do the work. tokens are still expensive today, but are likely to get cheaper over time (xitter.cf/theo/status/2107924138…) factoring in the cost of intelligence. what looks exorbitant today will likely be affordable in 6 moths to a year. a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost. the reality that i don't think has hit yet is that job of the software engineer has truly changed. our job is not merely to produce software any longer (well tbh it was never only about the code, but bear with me for the sake of the explanation), but the machine that writes the software. this idea of a software factory, or a Michelin kitchen as i like to call it, is still a topic of research. and it's the type of stuff i like to share, not to flex, but to show you what's possible when you put a lot of rigor into using agents at scale
Can someone help me understand how shipping 100s PRs a day makes sense? Why do you need to ship so many PRs in the first place? I might just not be AI-pilled enough to understand the need for it yet, so I’m genuinely trying to understand it.
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Brian Scanlan retweeted
reminder: you can just do things
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Brian Scanlan retweeted
We’re pleased to announce the launch of our 2.0 series of Fin Apex models! We think these are the best models for customer service. They are incredibly fast and efficient to run, but even beyond that, we’re seeing an increasing, not shrinking, performance gap between our specialised models, and the general frontier. We recently did a backtest against Astra, an amazing general model, and you’d expect overkill for customer service - but it still underperforms Apex on our particular tasks. So, at least in our experience, there’s still a lot of room for domain specific customisation. We try to give some flavour of the sort of tradeoffs necessary to achieve this performance in the launch post: fin.ai/blog/announcing-apex-…
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Brian Scanlan retweeted
Without reading the blog: “That’s right it goes into the S3 hole”?
The busiest single repo on GitHub saw roughly a billion requests in ONE MONTH. Today, we shared how we’re rebuilding GitHub’s storage layer (without changing how you use GitHub) decoupling compute & storage so reads and writes scale independently. Quicker pushes, pulls, and a platform that stays fast as more agents and developers code. We’re designing against some of the largest workloads on the platform so there’s headroom for everyone. More below on how this works and excited to show you more at @GitHub Universe. 👇
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Brian Scanlan retweeted
Context management is one of the biggest levers of improving Claude sessions quality and keeping costs in check. One of the core things we focus on @fin_ai engineering. I wrote about it on the Fin blog! finideas.substack.com/p/cont…
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Brian Scanlan retweeted
I've been loving pi-autoresearch from @davebcn87 and @tobi but have been back in Claude Code for my personal projects recently. In honour of @ClaudeDevs shipping mods I've made github.com/minuteman3/claude… to bring the workflow and ideas to Claude Code.
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Football, strategy, classics.
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Brian Scanlan retweeted
What do you do now that you’re not coding? For me it’s a ton of the same stuff as before, just with some things being a lot more impactful than before, like onboarding docs, linters, local emulators, and testers. Part 2: blog.dyanacek.com/2026/09/27…
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A Possible Explanation: The US knew there was a threat to RAF Fairford - that's not in dispute here. The FPCON changes, armed patrol vehicles inside the base and concrete barricades all show that. It seems possible to me that perhaps these 5 lads (or 8-10 if the eyewitness farmer is correct) were not actually there to attack the base - perhaps (given the fake fuel vans) they were actually in the area trying to steal fuel from local vehicles. The farmer sees it, calls police. Police (who likely were aware of the USAF concerns) swarm into the area having perhaps not expected the "attackers" so soon. They manage to arrest 5 suspects and treat the 3 vans with every necessary precaution (as they should) - evacuating the neighbourhood, bringing Army EOD in, etc. The males are initially arrested for Explosives Act offences (pending a safe way to check inside the vans) & go to custody. In the circumstances, further arrested for the Terrorism Act offence. However, after seizing whatever evidence they could (and examining the vans), it looks like police may have determined these aren't the attackers - as I say, potentially fuel thieves in the wrong place at the wrong time? It's worth noting that if the terrorism act offences were looking likely, police could keep them detained for 48hrs initially before requiring a court to approve a longer period of time. Alternatively under normal arrest powers contained within the Police and Criminal Evidence Act 1984, they can be held for 24hrs initially (extended to 36hrs under authority of a Police Superintendent if specific requirements are met), and up to 72hrs if a further extension is authorised by a court. Now clearly if they were arrested in the early hours of Sunday morning and conveyed either to a local custody, or a dedicated counter-terrorism custody (likely closest in London), the fact they've been bailed today suggests they've spent at most 24-30hrs in custody before evidential review officers and the Crown Prosecution Service have determined there is insufficient evidence to charge them with an offence right now. So they bail them with conditions not to contact each other as is normal procedure for vehicle interference/theft offences like that. But police need to download and analyse phones, and anything potentially seized from home addresses in order to ensure no link to the Iranian government or IRGC. Meanwhile, did the actual planned attack get foiled inadvertently? Perhaps? Trump told the world that the US had been monitoring the "attackers" for some time - I'd suggest that if these lads genuinely were just in the wrong place at the wrong time, the actual suspects are now staying hidden until this all blows over a bit. Either way, we don't have all the facts and that's part of any criminal or CT investigation - so yes, it sounds bad and arguably CTP could have done a better job of explaining it to the public, but they have to play their cards close to their chest right now in case my guesswork here is right and the suspects are still out there somewhere.
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We believe superintelligence will create significant new opportunities for all people and businesses. Meta already serves billions of people at scale and helps hundreds of millions of businesses reach customers. Today we are starting the next major pillar of our business, Meta Enterprise Platform, to help businesses use AI to grow and transform in new ways as well.
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Maybe old news? lethain.com/migrations/
We rewrote an internal thing from Zig to JavaScript and it got faster and more reliable. The obvious conclusion is that language doesn’t matter. Rewriting does. You should be rewriting everything all the time.
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Brian Scanlan retweeted
I have been managing our claude settings, best practices & plugins ecosystems @fin_ai for quite some time. We have just started introducing codex into the mix and there are some interesting things I observed while trying to figure out compatibility 👇
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Brian Scanlan retweeted
We ended up rolling out a deterministic hook that slaps Claude on the wrist with non-zero exit code and a little context injection on how we want our code comments (no comments is the best). Result on the screenshot - we are back to pre-Opus 4.7 comment % in our PRs!
We've been fighting with Claude generating lots of confusing code comments. Here's a timing correlation between Opus 4.8 rollout and the % of comment lines (2x bump!) – this is based on 42k merged PRs assessed by our code review bot. Anyone have any solutions that worked?
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