Rust Backend Developer & Cloud Engineer | Rustacean 🦀

Lagos, Nigeria
Joined September 2011
The courage to try, the humility to learn, the will to never give up.
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Allwell retweeted
I like what DeepSeek is doing. - They have a top of the line open-model. - Top of the line harness. - And now top of the line inference engine. All open-source and available out of the open. Thumbs up for them, I love it.
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Allwell retweeted
The Tesla Roadster event was so epic that it needed it's own music video Introducing "FLYING CARS" featuring Optimus 🔥
🤖 Made with AI
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Allwell retweeted
Tesla Event - Short Version Die verrückteste Produktvorstellung aller Zeiten. Elon Musk zeigt fliegenden Roadster. 0 - 100 km/h in unter 1 Sekunde. Das war's für Ferrari, Bugatti und Co. Dieses Auto wird jeden Rundenrekord brechen.
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Allwell retweeted
it is absolutely insane what elon has done for humanity it actually makes no sense electric, self-driving, flying cars -- openai, reusable spaceships, planetary expansion, new internet network that decides wars, paypal, x, solar, neuralink, hyperloop brother how???
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All over the country, kids just watched the Roadster fly and decided their own crazy ideas might be possible too. Thanks, @elonmusk.
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Allwell retweeted
Imagine how many kids watched what happened at SpaceX tonight and want to become engineers. This is good for humanity.
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Elon has done so much for humanity Even tonight. Consider every inspired engineer, builder, creator that wants to go harder and think deeper from today’s presentation Man. What a time to be alive
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DHH
@dhhOct 16
If your first response to the Tesla Roadster is "is that safe??", you've lost all sense of wonder. Safety is not and never was the meaning of life. I'd happily spend a few micromorts on a flying car. (Also, Tesla has a sterling safety record, so probably won't even need to).
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Only in America can the impossible be made possible.
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Elon Musk
@elonmuskOct 15
Maybe we should start a competitor to Amazon 🤔
amazon is blocking grok @bots from ordering, and elon's answer is just have your bot buy it somewhere else. one of my grok bots only handles my amazon returns, so i'm watching this one closely. would you switch stores for your bot? 🛒 Elon, i think its time for an X Shop? 👀
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Anthropic
@AnthropicAIOct 15
Applications are now open for two new Anthropic Fellowships. STEM Fellows (science and engineering) and Conceptual Reasoning Fellows (philosophy, cognitive science, economics, policy, and law) will spend four months at Anthropic from January 2027, working to help Claude reason well about some of the hardest problems in those fields. For more information, see the descriptions below. The application deadline is October 28. Conceptual Reasoning Fellows: job-boards.greenhouse.io/ant… STEM Fellows: job-boards.greenhouse.io/ant…
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Allwell retweeted
Elon did it all in one life
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Elon Musk
@elonmuskOct 15
New Roadster is beyond movies, beyond imagination, beyond human, yet still made by humans on Earth
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Claude Code vs Developers 😂
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Your ego will cost you the love of your life
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Anthropic
@AnthropicAIOct 13
New on our Engineering blog: Creating in-house source control. In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale. Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
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DHH
@dhhOct 12
Dario turned out to be right. Maybe even a little cautious. But look at the comments. All so very convinced that they knew more and better, and what an idiot he was.
"Software Engineering Will Be Automatable in 12 Months," Anthropic CEO Dario Amodei predicts that AI models will be able to do 'most, maybe all' of what software engineers do end-to-end within 6 to 12 months, shifting engineers to editors.
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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.
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Allwell retweeted
hot take: by freeing us for the hassle of writing code, SWE is now more about engineering than it has ever been
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