Toomas Römer

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Toomas Römer

Toomas Römer

@toomasr

Dad, VP of Engineering @ Bolt Technology, Co-founder of ZeroTurnaround, Geek, Hackepreneur, Baduk & Tennis player

Estonia Katılım Şubat 2008
1.2K Takip Edilen1.1K Takipçiler
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Andrej Karpathy
Andrej Karpathy@karpathy·
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
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Toomas Römer
Toomas Römer@toomasr·
And after many (5?) years of waiting, #lossless has landed on #spotify and as of today is also enabled for my account. You'll see a cue under the artist/title, but it will take a few minutes to pick it up after enabling it in settings.
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Markus Villig
Markus Villig@villigm·
We just opened Bolt’s new HQ in Tallinn! ✂️ This building has a strong legacy: it was home to @Wise for many years including their IPO. Now it’s our time to make history here. As always, I'm on the lookout for top talent in Tallinn. And now we have the space to welcome you.
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Lord Haydock
Lord Haydock@MarkHaydock5·
Just in case you were wondering 🤓
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Jordan Taylor
Jordan Taylor@Jordan_W_Taylor·
How do you create Nothing at all? And once you've created Nothing, how do you measure it? It's a lot harder than you think… Creating the domain of gods: This is the vacuum system thread!
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World of Engineering
World of Engineering@engineers_feed·
1. Go to Google 2. Search Cha Cha Slide 3. Touch the mic icon 4. Touch anywhere You’re welcome 🎤
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Toomas Römer
Toomas Römer@toomasr·
@bsideup Oh, then it is one that is actually quite sustainable! I'm just counting calories one day a month to remind me how much I consume when I eat A, B or C. No real progress with the weight but I'm more conscious when I overeat 🥸
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Sergei Egorov
Sergei Egorov@bsideup·
@toomasr Thank you, Toomas! Not as crazy as pre-ZT -20kg tho 😅 The diet is not very specific and I would not recommend it to anyone, but the idea is that you are on calories deficit, plus hydrate like crazy. Meaning low sugars (*zero* added), no alcohol, protein over carbs, etc
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Sergei Egorov
Sergei Egorov@bsideup·
Started the diet a month ago. Minus 7kg/15lb, no longer overweight. Feels good! Sharing to motivate others, as I got motivated myself after seeing some tweets 🙌
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Toomas Römer
Toomas Römer@toomasr·
@GergelyOrosz Besides disliking HP and liking Brother the pictured printer looks more like HL-L2400DW and not 2350. My 2350 died on me randomly last week after 4yrs but I still chose Brother because of the reasons in the article and thread. Upgrade to 2400 gave me 5ghz wifi support I believe
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Gergely Orosz
Gergely Orosz@GergelyOrosz·
Love this Verge article. Used to have HP printers until HP introduced dark patterns: the cartridge more expensive than the printer / cartridge half full but printer refuses to print / HP blocks refilled cartridges. Switched to Brother: best decision. If you’re still on HP: why?
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Jordan Taylor
Jordan Taylor@Jordan_W_Taylor·
When 460 knots just isn't fast enough... We've been cruising the globe at basically the same speed since the 1960s. Faster would be nicer, so what are the challenges facing hypersonic airliners? Read on...
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Douglas Mun
Douglas Mun@douglasmun·
*Evolution of click farm fraud.* 1st generation click farm fraud, fully manual labour.
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Toomas Römer
Toomas Römer@toomasr·
Actually we have the same at @boltapp and I'm glad that there is such a website now. For trolling purposes I will continue saying a single "hi" every now and then 🥸
Ben Lang@benln

Every startup should incorporate the “no hello” rule internally. We had this rule in Notion’s first internal communication guidelines. This may be one of the easiest ways to make Slack usage ten times better and have asynchronous communication that actually works. Share nohello.net with your team and give it go.

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Andrej Karpathy
Andrej Karpathy@karpathy·
# automating software engineering In my mind, automating software engineering will look similar to automating driving. E.g. in self-driving the progression of increasing autonomy and higher abstraction looks something like: 1. first the human performs all driving actions manually 2. then the AI helps keep the lane 3. then it slows for the car ahead 4. then it also does lane changes and takes forks 5. then it also stops at signs/lights and takes turns 6. eventually you take a feature complete solution and grind on the quality until you achieve full self-driving. There is a progression of the AI doing more and the human doing less, but still providing oversight. In Software engineering, the progression is shaping up similar: 1. first the human writes the code manually 2. then GitHub Copilot autocompletes a few lines 3. then ChatGPT writes chunks of code 4. then you move to larger and larger code diffs (e.g. Cursor copilot++ style, nice demo here youtube.com/watch?v=Smklr4…) 5.... Devin is an impressive demo of what perhaps follows next: coordinating a number of tools that a developer needs to string together to write code: a Terminal, a Browser, a Code editor, etc., and human oversight that moves to increasingly higher level of abstraction. There is a lot of work not just on the AI part but also the UI/UX part. How does a human provide oversight? What are they looking at? How do they nudge the AI down a different path? How do they debug what went wrong? It is very likely that we will have to change up the code editor, substantially. In any case, software engineering is on track to change substantially. And it will look a lot more like supervising the automation, while pitching in high-level commands, ideas or progression strategies, in English. Good luck to the team!
YouTube video
YouTube
Cognition@cognition

Today we're excited to introduce Devin, the first AI software engineer. Devin is the new state-of-the-art on the SWE-Bench coding benchmark, has successfully passed practical engineering interviews from leading AI companies, and has even completed real jobs on Upwork. Devin is an autonomous agent that solves engineering tasks through the use of its own shell, code editor, and web browser. When evaluated on the SWE-Bench benchmark, which asks an AI to resolve GitHub issues found in real-world open-source projects, Devin correctly resolves 13.86% of the issues unassisted, far exceeding the previous state-of-the-art model performance of 1.96% unassisted and 4.80% assisted. Check out what Devin can do in the thread below.

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Elon Musk
Elon Musk@elonmusk·
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Toomas Römer@toomasr·
It is this time of the year again #SpotifyWrapped Always fun to see the metrics and again quite surprising results for me ...
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Toomas Römer
Toomas Römer@toomasr·
I love great hospitality. I managed to get the warmest welcome ever! The hotel room AC had been set to 29.5C 🥵 I guess it is the thought that counts 🤣
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