Mayowa Adeoni @_maycode

I built something I believe is useful for agentic coding today. I’ve been too worried it’s not the best it can be, but my wife encouraged me to fail fast if I was worried about failure, so here we are. Introducing Kontinuo by @pullbase, the continuity layer for your AI coding agents. You’re mid-task, you hit a rate limit on Claude, and you want to keep going on Codex or Cursor. But the next agent has no idea what just happened. Kontinuo fixes that, it leaves a verifiable context log the next agent can actually use and it’s really token efficient. Check it out: kontinuo.dev Use it, break it. I hope it’s useful to at least one person.

Tooooor At least, there won't be noise pollution tonight 😭 PSG really said "mute all notifications." #UCLFinal #PSGArsenal #ChampionsLeague #PSG #Arsenal #FootballTwitter #UCL #PSGvsArsenal

I just claimed my .agent domain and joined Agent Community! get yours now and help shape the future of autonomous agents agentcommunity.org/join#4GIDCQY0 @agentcommunity_

Introducing Flue — The First Agent Harness Framework Flue is a TypeScript framework for building the next generation of agents, designed around a built-in agent harness. Flue is like Claude Code, but 100% headless and programmable. There's no baked in assumption like requiring a human operator to function. No TUI. No GUI. Just TypeScript. But using Flue feels like using Claude Code. The agents you build act autonomously to solve problems and complete tasks. They require very little code to run. Most of the "logic" lives in Markdown: skills and context and AGENTS.md. Flue is like Astro or Next.js for agents (not surprising, given my background 🙃). It's not another AI SDK. It's a proper runtime-agnostic framework. Write once, build, and deploy your agents anywhere (Node.js, Cloudflare, GitHub Actions, GitLab CI/CD, etc). We originally built Flue to power AI workflows inside of the Astro GitHub repo. But then @_bgiori got his hands on it, and we realized that every agent needs a framework like Flue, not just us. Check it out! It's early, but I'm curious to hear what people think. Are agents ready for their library -> framework moment?

Mathematics is amazing because they knew exactly where the splash down would be. That’s actually wild considering at some point they were going at a speed of 24,000 mph Read this to nerd out on it How NASA Achieved the Historic Artemis II Splashdown time.com/article/2026/0…

@OneBankNG @OneBankNG can you guys fix your bank?! It's really not so good experience!

I have so much gratitude to people who wrote extremely complex software character-by-character. It already feels difficult to remember how much effort it really took. Thank you for getting us to this point.

"A computer is becoming a thing computers use" Really looking forward to where this leads us....

Naval @naval

4 months ago

A “computer” used to be a job title. Then a computer became a thing humans used. Now a computer is becoming a thing computers use.

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The past three months have moved faster than anything I’ve seen in software engineering since I started coding around 2011. Everything has changed! Reminds me of the saying: "There are decades where nothing happens; and there are weeks where decades happen".

Be competent beyond question, beyond doubt, beyond excuse. Become the yardstick for quality. This is what you owe your team. This is all I ask.

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@utdreport Plays like Greenwood

As a software engineer, I think understanding money is critical. Most engineers just want to code. They build features, feel satisfied, and call it a day. Then the feature gets killed because it never moved the business forward. The engineers who move up fast simply understand how their code impacts revenue. In 2026, AI writes solid code faster than most people, but AI cannot accurately answer questions like: - Will this feature increase revenue? - Is this bug causing customers to leave? - Will this optimization cut costs? Engineers who prioritize these questions progress quicker. To think like this, learn the company's business model thoroughly. SaaS means tracking MRR, churn, expansion. E-commerce means conversion rates, AOV, LTV. The real numbers matter. On every feature or ticket, consider how it affects key metrics. If there is no obvious link, it is likely low priority. Shift from saying: "Built notification system" To saying: "Improved retry logic, recovered 12% failed notifications, added $5k monthly revenue" Numbers change how impact is measured. From my experience, engineers who connect code to business outcomes win in the long run.

@1Doneazy Tbh, I never knew it caused migraines. I just thought it was always fatigue from working long hours in front of a screen.

I used to think my eyes were “strong” because I could stare at screens for hours without anti-glare glasses. I’ve owned two pairs for 4 years but barely used them.

If you spend long hours on screens, protect your eyes early. It matters.

Recently, I noticed long screen time actually drains me. Since I started wearing my glasses consistently, the eye strain is reduced and I feel less tired.

Advice for entry-level software engineers: Focus on understanding how computers work. Learn how they transmit, process, and store data. Fundamentals are more important than the latest frameworks. Right now, AI can generate code, but it cannot solve the problem for you. To effectively instruct the tool, you must understand the building blocks. 1. Start with Networking. Don't just verify that an API works. Understand how the data gets there. - HTTP/HTTPS: Learn the request lifecycle. - DNS: How names become IP addresses. - TCP vs. UDP: Reliability vs. Speed. If you don't understand the transport, you can't debug the latency. 2. Master the Operating System, specifically Linux. Most of the cloud runs on Linux. You need to be comfortable in the terminal. - File Systems: Everything is a file. - Process Management: How programs start, run, and die. - Memory Management: Stack vs. Heap. All code needs an OS to run. 3. Understand Data Structures (and when to use them). This isn't about passing interview tests; it's about performance. - Know why a Hash Map is faster than an Array for lookups. - Understand Big O notation to predict how your code behaves as user traffic grows. Inefficient code costs money. 4. Deep dive into Databases. Storing data is easy; retrieving it quickly is the hard part. - Indexing: How to make queries fast. - ACID: Understanding transaction integrity. - Normalization: How to structure data to avoid redundancy. Bad schema design creates tech debt that is painful to fix later. The best engineers I know aren't the ones who know every syntax of a new language. They are the ones who can understand systems thinking & can visualize the path of a byte from the user's click to the hard drive and back. “Vision without systems thinking ends up painting lovely pictures of the future with no deep understanding of the forces that must be mastered to move from here to there. — Peter Senge, The Fifth Discipline (Currency)” Focus on the mechanics, and the tools will make sense. Happy New Year, May the force be with you!

It didn't make me faster. It made me appreciate the power of strong fundamentals.

The “easy” stuff feels different when you understand the hard work underneath it.

We call C “old,” but it quietly powers everything we call modern; the web, frameworks, even AI systems. Most operating systems, compilers, and language runtimes are written in C.