AI

• Don’t sleep on OpenClaw. There are a ton of people building with it right now who aren’t talking about it yet. The potential is real, and when those projects start surfacing, it’s going to turn heads. Sometimes the most exciting stuff happens quietly before it hits the mainstream.

  Feb 14, 2026 / AI / Open-source / Openclaw

• REPL-Driven Development Is Back (Thanks to AI)

  So you’ve heard of TDD. Maybe BDD. But have you heard of RDD?

  REPL-driven development. I think most programmers these days don’t work this way. The closest equivalent most people are familiar with is something like Python notebooks—Jupyter or Colab.

  But RDD is actually pretty old. Back in the 70s and 80s, Lisp and Smalltalk were basically built around the REPL. You’d write code, run it immediately, see the result, and iterate. The feedback loop was instant.

  Then the modern era of software happened. We moved to a file-based workflow, probably stemming from Unix, C, and Java. You write source code in files. There’s often a compilation step. You run the whole thing.

  The feedback loop got slower, more disconnected. Some languages we use today like Python, Ruby, JavaScript, PHP include a REPL, but that’s not usually how we develop. We write files, run tests, refresh browsers.

  Here’s what’s interesting: AI coding assistants are making these interactive loops relevant again.

  The new RDD is natural language as a REPL.

  Think about it. The traditional REPL loop was:

  1. Type code
  2. System evaluates it
  3. See the result
  4. Iterate

  The AI-assisted loop is almost identical:

  1. Type (or speak) your intent in natural language
  2. AI interprets and generates code
  3. AI runs it and shows you the result
  4. Iterate

  You describe what you want. The AI writes the code. It executes. You see what happened. If it’s not right, you clarify, and the loop continues.

  This feels fundamentally different from the file-based workflow most of us grew up with. You’re not thinking about which file to open, You’re thinking about what you want to happen, and you’re having a conversation until it does.

  Of course, this isn’t a perfect analogy. With a traditional REPL, you have more control. You understood exactly what was being evaluated because you wrote it.

   >>> while True:
    ...     history.repeat()
  

  Feb 13, 2026 / AI / Programming / Development

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• I usually brainstorm spec docs using Gemini or Claude, so if you are like me, this prompt is interesting insight into your software decisions.

  Based off our previous chats and the previous documents you've helped me with, provide a detailed summary of all my software decisions and preferences when it comes to building different types of applications.

  Feb 12, 2026 / AI / Development

• Here’s a tip: if you ask Claude (via an API not Code) to Vibe a typing hacker game make sure to tell it not to return valid exploits. I asked Claude to use actual Python code snippets in the game today and… GitHub’s security scanner was not happy with me. Oopsie doopsie. Lesson learned!

  Feb 12, 2026 / AI / Claude / Coding

• Knowledge Without a Knower

  How do we define knowledge in the age of AI? Can new knowledge even be created if we’re outsourcing our thinking to the models or the systems we built around the models?

  Let’s start with what knowledge actually is. Traditionally, to know something, you have to believe it’s true and have some justification for that belief. It’s implicit knowledge earned through experience, study, or reasoning.

  AI doesn’t work that way. To the tools, it’s a probabilistic map of patterns extracted from massive amounts of text. There’s no belief, no understanding in the human sense. It’s knowledge without a knower.

  That distinction matters more than we might think.

  From Retention to Curation

  The way we work with knowledge is shifting. For centuries, the paradigm was retention: memorize facts, write things down, build personal libraries of information.

  Now we have tools that can do that for us, often better and faster than we ever could.

  So what’s our new role?

  Curation.

  The skills that matter now are about what we can retrieve, what we can verify, and what we can synthesize.

  We don’t need to remember everything, we need to know how to find it, evaluate it, and combine it in useful ways.

  The Skills We Actually Need

  If we’re not going to be the primary repositories of knowledge anymore, what should we focus on?

  Spotting bullshit. This might be the most important skill of the next decade. When the tool outputs something that doesn’t match what we know to be true, can we catch it? AI systems are confident even when they’re wrong. They don’t hedge. They don’t say “I’m not sure about this.” So we need that internal alarm that goes off when something doesn’t add up.

  Asking good questions. This has always been important, but it’s now essential. Understanding the problem means knowing where the gaps in your knowledge actually lie. A well-formed question is half the answer. An AI can give you a thousand responses, but only a good question will get you a useful one.

  Reasoning about reasoning. How did the system arrive at that answer? What steps did it take? Why does it think that’s the case? We need to be able to trace the logic, not just accept the output. This is meta-cognition applied to our tools.

  The Human in the Loop

  New knowledge will continue to need humans. Not for the grunt work of data processing or pattern matching, AI can handle that better than we ever could.

  Instead our role is to identify the anomalies. We need to become detectives, finding the errors in the data. Skepticism will be extremely valuable in the times ahead.

  Being a critical thinker. We need to be able to evaluate the evidence, weigh the pros and cons, and make informed decisions.

  In computing, we see error correcting used in the semiconductor industry, and we see a different technique also used in the quantum computing industry. And while reducing the amount of errors in a given system will continue to be important, what we really after here?

  Well, the truth, right?

  I propose we come up with a new name for truth. I think it should be called “HAT” or a “human accepted truth.”

  The aggregate of HATs is what we shall call “knowledge.” Knowledge is the sum of all human accepted truths.

  Feb 10, 2026 / AI / Knowledge / Thinking

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• The Broken Promise of Reach

  AI is changing things we take for granted: the relationship between effort and reach.

  For years, the implicit promise of the internet was straightforward.

  Put in the work, create something valuable, and you’d find your audience. Maybe not millions, but someone.

  The effort you invested had a reasonable correlation to the impact you could achieve. A thoughtful blog post might get shared.

  A well-crafted tutorial could help thousands of developers. The work mattered because it reached people who needed it.

  That equation is no longer guaranteed.

  Now we’re in a world where AI can generate endless content at near-zero cost.

  The supply of words, images, and ideas has become functionally infinite.

  So, what happens to the value of any individual piece?

  • Your carefully researched article drowns in a database of a thousand AI-generated summaries.
  • Your authentic ideas are lost in a sea of algorithmic content designed for engagement.

  So, why put in the work if the reward isn’t there?

  The dream used to be building something sustainable or something big enough to matter. Enough to support yourself while doing work you care about. And it still can be, that, a dream.

  We need to return to the act of finding value in the act itself

  Don’t let your self-worth depend on metrics decided by a platform.

  Your entire creative output shouldn’t be measured in likes, shares, and subscriber counts. That’s when they win.

  We can’t hand over the definition of “success” to the algorithms

  The old promise of platforms will provide is broken.

  We’re not going back.

  Now we need to build something new.

  What we build is up to us.

  Feb 8, 2026 / AI / Indieweb / Publishing

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• First Impressions of OpenAI's Codex App

  I’ve been experimenting with OpenAI’s new Codex app for engineering work that launched on February 2nd, and I’m not impressed.

  No subagents from what I can tell. It gets stuck on stuff that shouldn’t be blockers. The gpt-5.2-codex model feels slow. I don’t care about the existing skills enough to try to set one up for it.

  I did sign up for a free month trial of ChatGPT Plus though, so I’m going to give it a few more attempts before my time runs out. But so far, it doesn’t feel like a force multiplier the way Claude Code or Amp Code does. Even Open Code feels more productive.

  Maybe I’ll have better luck with Codex on the CLI? We’ll see.

  There’s something about flat-fee billing that feels so much better than watching tokens drain away. Less constrained and more open to trying new things I guess.

  I appreciate that they went through the effort of building what looks like a native Swift app instead of yet another VS Code fork.

  I think Codex desktop app is aimed at competing with Cursor’s market share, or maybe it’s some attempt at solving whatever direction Claude Desktop is heading.

  It doesn’t feel like it’s solving my problems as a developer who already has workflows that I know work.

  Back to the CLI pour moi.

  Feb 6, 2026 / AI / Developer-tools / Openai

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• Why IndiePub Matters More Than Ever

  You might have heard the term IndiePub. It’s short for independent publishing—the practice of creating and distributing your work (books, stories, games, articles) without the financial backing or editorial control of a larger corporation.

  It’s closely related to the IndieWeb movement in software development, which has been advocating for personal ownership of digital presence for years.

  In the last two decades, we’ve watched massive platforms rise to dominance. They promised reach and convenience, and they delivered—for a while. But now we’re seeing the consequences: algorithmic timelines that bury your work, arbitrary policy changes that can wipe out years of audience building, and the degradation of platform quality as engagement-farming content floods every feed.

  This platform decay is exactly why anyone publishing content on the internet should own their own distribution.

  I’m not saying you have to stop publishing on platforms. But you shouldn’t publish there first.

  Publish your work on something you control, your own site, your own domain, and then push it out to the platforms. Your home base stays yours. The platforms get a copy.

  The AI Content Flood Changes Everything

  Now we’re facing something new, with the rise of AI-generated content, these platforms are becoming saturated with noise. Human authenticity is at a premium.

  The irony? Human authentic content is what trained these large language models in the first place.

  So there’s a reclaiming happening here. We’re taking our data back.

  Our authentic human content belongs on the platforms we control. The AI stuff can continue flowing through the corporate channels.

  It’s Never Been Easier

  I’m not going to dive deep into the complexities of book publishing or all the decisions writers face when navigating traditional vs. self-publishing. But I will say this: it’s never been easier to find a way to publish your thoughts and ideas online.

  There are definitely some better decisions you can make about where and how to publish. If you’re wondering whether you’ve made the best choice for the type of content you want to put out there, I created a guided decision-making tool to help answer those questions: blog-picker.logan.center

  ---

  IndiePub matters now more than ever because it’s our primary defense against the commoditization of creativity.

  When everything becomes content for platforms, when algorithms decide what gets seen, when AI can generate infinite variations of “good enough” content.

  Own your words. Own your distribution. The platforms are guests at your table, not the other way around.

  Feb 5, 2026 / AI / Indieweb / Publishing / Creativity

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• Why Data Modeling Matters When Building with AI

  If you’ve started building software recently, especially if you’re leaning heavily on AI tools to help you code—here’s something that might not be obvious: data modeling matters more now than ever.

  AI is remarkably good at getting the local stuff right. Functions work. Logic flows. Tests pass. But when it comes to understanding the global architecture of your application? That’s where things get shaky.

  Without a clear data model guiding the process, you’re essentially letting the AI do whatever it thinks is best. And what the AI thinks is best isn’t always what’s best for your codebase six months from now.

  The Flag Problem

  When you don’t nail down your data structure upfront, AI tools tend to reach for flags to represent state. You end up with columns like is_draft, is_published, is_deleted, all stored as separate boolean fields.

  This seems fine at first. But add a few more flags, and suddenly you’ve got rows where is_draft = true AND is_published = true AND is_deleted = true.

  That’s an impossible state. Your code can’t handle it because it shouldn’t exist.

  Instead of multiple flags, use an enum: status: draft | published | deleted. One field. Clear states. No contradictions.

  This is just one example of why data modeling early can save you from drowning in technical debt later.

  Representation, Storage, and Retrieval

  If data modeling is about the shape of your data, data structures determine how efficiently you represent, store, and retrieve it.

  This matters because once you’ve got a lot of data, migrating from one structure to another, or switching database engines—becomes genuinely painful.

  When you’re designing a system, think about its lifetime.

  • How much data will you store monthly? Yearly?
  • How often do you need to retrieve it?
  • Does recent data need to be prioritized over historical data?
  • Will you use caches or queues for intermediate storage?

  Where AI Takes Shortcuts

  AI agents inherit our bad habits. Lists and arrays are everywhere in their training data, so they default to using them even when a set, hash map, or dictionary would perform dramatically better.

  In TypeScript, I see another pattern constantly: when the AI hits type errors, it makes everything optional.

  Problem solved, right? Except now your code is riddled with null checks and edge cases that shouldn’t exist.

  Then there’s the object-oriented problems. When building software that should use proper OOP patterns, AI often takes shortcuts in how it represents data. Those shortcuts feel fine in the moment but create maintenance nightmares down the road.

  The Prop Drilling Epidemic

  LLM providers have optimized their agents to be nimble, managing context windows so they can stay productive. That’s a good thing. But that nimbleness means the agents don’t always understand the full structure of your code.

  In TypeScript projects, this leads to prop drilling: passing the entire global application object down through nested components.

  Everything becomes tightly coupled. When you need to change the structure of an object, it’s like dropping a pebble in a pond. The ripples spread everywhere.

  You change one thing, and suddenly you’re fixing a hundred other places that all expected the old structure.

  The Takeaway

  If you’re building with AI, invest time in data modeling before you start coding. Define your data structures. Think about how your data will grow and how you’ll access it.

  The AI can help you build fast. But you still need to provide the architectural vision. That’s not something you can blindly trust the AI to handle, not yet, anyway.

  Feb 4, 2026 / AI / Programming / Software-development

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• 2026: The Year We Stop Blaming the Tools

  Here’s a hard truth we’re going to have to face in 2026: sometimes the bottleneck isn’t the technology, it’s us.

  I’ve been thinking about how we use tools, how we find the merit in their use. We have access to increasingly powerful tools, but their value depends entirely on our understanding of them.

  A hammer is useless if you don’t know which end to hold. The same goes for AI assistants, automation frameworks, and the growing ecosystem of agentic systems.

  The rapid adoption of tools like OpenClaw’s agentic assistant tells me something important: people and companies are starting to see the real potential in building autonomous systems. Not just as toys or experiments, but as genuine productivity multipliers. That’s a shift from where we were even a year ago.

  I think 2026 will be the year we see more widespread adoption of genuinely useful tools. The Gartner hype cycle is really interesting and how it applies or doesn’t to AI adoption, but I won’t cover it here. I’d like to write more about that in future articles.

  The companies that build genuinely useful tools will be the ones that survive. They’ll be the ones that understand the value of tools and how to use them effectively. They’ll be the ones that embrace the future of work, where humans and machines work together to achieve more.

  It’s not about replacing humans. It’s about humans getting better at wielding the tools we’ve built. That’s always been how technology works. This time is no different.

  Feb 1, 2026 / AI / Tools / automation / 2026

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• The Rise of Spec-Driven Development: A Guide to Building with AI

  Spec-driven development isn’t new. It has its own Wikipedia page and has been around longer than you might realize.

  With the explosion of AI coding assistants, this approach has found new life and we now have a growing ecosystem of tools to support it.

  The core idea is simple: instead of telling an AI “hey, build me a thing that does the boops and the beeps” then hoping it reads your mind, you front-load the thinking.

  It’s kinda obvious, with it being in the name, but in case you are wondering, here is how it works.

  The Spec-Driven Workflow

  Here’s how it typically works:

  1. Specify: Start with requirements. What do you want? How should it behave? What are the constraints?

  2. Plan: Map out the technical approach. What’s the architecture? What “stack” will you use?

  3. Task: Break the plan into atomic, actionable pieces. Create a dependency tree—this must happen before that. Define the order of operations. This is often done by the tool.

  4. Implement: You work with whatever tool to build the software from your task list. The human is (or should be) responsible for deciding when a task is completed.

  You are still a part of the process. It’s up to you to make the decisions at the beginning. It’s up to you to define the approach. And it’s up to you to decide you’re done.

  So how do you get started?

  The Tool Landscape

  The problem we have now is there is not a unified standard. The tool makers are busy building the moats to take time to agree.

  Standalone Frameworks:

  • Spec-Kit - GitHub’s own toolkit that makes “specifications executable.” It supports multiple AI agents through slash commands and emphasizes intent-driven development.

  • BMAD Method - Positions AI agents as “expert collaborators” rather than autonomous workers. Includes 21+ specialized agents for different roles like product management and architecture.

  • GSD (Get Shit Done) - A lightweight system that solves “context rot” by giving each task a fresh context window. Designed for Claude Code and similar tools.

  • OpenSpec - Adds a spec layer where humans and AI agree on requirements before coding. Each feature gets its own folder with proposals, specs, designs, and task lists.

  • Autospec - A CLI tool that outputs YAML instead of markdown, enabling programmatic validation between stages. Claims up to 80% reduction in API costs through session isolation.

  Built Into Your IDE:

  The major AI coding tools have adopted this pattern too:

  • Kiro - Amazon’s new IDE with native spec support
  • Cursor - Has a dedicated plan mode
  • Claude Code - Plan mode for safe code analysis
  • VSCode Copilot - Chat planning features
  • OpenCode - Multiple modes including planning
  • JetBrains Junie - JetBrains' AI assistant
  • Google Antigravity - Implementation planning docs
  • Gemini Conductor - Orchestration for Gemini CLI

  Memory Tools

  • Beads - Use it to manage your tasks. Works very well with your Agents in Claude Code.

  Why This Matters

  When first getting started building with AI, you might dive right in and be like “go build thing”. You keep then just iterating on a task until it falls apart once you try to do anything substantial.

  You end up playing a game of whack-a-mole, where you fix one thing and you break another. This probably sounds familiar to a lot of you from the olden times of 2 years ago when us puny humans did all the work. The point being, even the robots make mistakes.

  Another thing that you come to realize is it’s not a mind reader. It’s a prediction engine. So be predictable.

  What did we learn? With spec-driven development, you’re in charge. You are the architect. You decide. The AI just handles the details, the execution, but the AI needs structure, and so these are the method(s) to how we provide it.

  Jan 31, 2026 / AI / Programming / Tools / Development

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• AMP Code: First Impressions of a Claude Code Competitor

  I tried AMP Code last weekend and came away genuinely impressed. I didn’t think there was anything at Claude Code’s level currently available.

  That said, AMP is in a somewhat unfortunate position. Similar to Cursor, they have to pay the Anthropic tax, and you really want your primary model to be Opus 4.5 for the best results.

  So while I was able to get some things done, once you start paying per token… you feel constrained. I’m speaking from a personal budget perspective here, but I blew through ten dollars of credits on their free tier pretty easily.

  I could see how with billing enabled and all the sub-agents they make super easy to use, you could burn through a hundred-dollar Claude Code Max plan budget in a week, or even a day, depending on your usage.

  What I Really Like

  There’s a lot to appreciate about what AMP is doing.

  Team collaboration is a standout feature. It’s incredibly easy to share a discussion with other people on your team. Being able to collaborate with your team on something using agents is extremely powerful.

  Their TUI is exceptional. I mean, it’s so much better than Claude Code’s terminal interface. They probably have the best TUI on the market right now. It’s definitely better than Open Code.

  Sub-agents work out of the box. All the complicated sub-agent stuff I’ve set up manually for my Claude Code projects? It just comes ready to go with AMP. They’ve made really smart decisions about which agents handle which tasks and which models to use. You don’t have to configure any of it, it’s all done for you.

  The Bottom Line

  I think for enterprise use cases, AMP Code is going to make a lot of sense for a lot of companies.

  For individual developers on a personal budget, the cost model is something to think carefully about.

  Jan 30, 2026 / AI / Developer-tools / Coding-assistants

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• Here’s a helpful idea on how to use Claude CoWork to update your photos EXIF data so they have descriptions.

  📸 Full writeup

  Jan 29, 2026 / AI / Photography

• Teaching AI to Hide Messages in Plain Sight

  It’s more of a party trick than anything else, but here’s the prompt:

  Task: Construct a perfectly rectangular 4x26 ASCII block.
  
  The "Safe-Width" Symbol Set: ∫ √ ≈ ∆ ∑ ± ∞ ≠ ≡ ≥ ≤ ÷ ç ∂
  
  Instructions:
  - Grid Specs: Exactly 4 rows and 26 characters per row
  - Hidden Message: Choose a 2-word uppercase phrase
  - Center the first word in Row 2 and the second in Row 3
  - Fill all non-letter slots with random symbols from the set
  - Verify each row is exactly 26 characters
  

  Those specific mathematical symbols maintain consistent width with uppercase Latin characters in monospaced fonts. This means the grid stays perfectly rectangular, and the words blend in better.

  Is this useful? Not really. But it’s a neat demonstration of how to get AI to produce structured output.

  Try it yourself and see what secret phrases you can embed.

  Jan 27, 2026 / AI / Prompts / Fun

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• I’m reading an article today about a long-term programmer coming to terms with using Claude Code. There’s a quote at the end that really stuck with me: “It’s easy to generate a program you don’t understand, but it’s much harder to fix a program that you don’t understand.”

  I concur, while building it may be fun, guess what? Once you build it, you got to maintain it, and as a part of that, it means you got to know how it works for when it doesn’t.

  Jan 26, 2026 / AI / Programming / Claude-code

• Security and Reliability in AI-Assisted Development

  You may not realize it, but AI code generation is fundamentally non-deterministic. It’s probabilistic at its core, it’s predicting code rather than computing it.

  And while there’s a lot of orchestration happening between the raw model output and what actually lands in your editor, you can still get wildly different results depending on how you use the tools.

  This matters more than most people realize.

  Garbage In, Garbage Out (Still True)

  The old programming adage applies here with renewed importance. You need to be explicit with these tools. Adding predictability into how you build is crucial.

  Some interesting patterns:

  • Specialized agents set up for specific tasks
  • Skills and templates for common operations
  • Orchestrator conversations that plan but don’t implement directly
  • Multiple conversation threads working on the same codebase via Git workspaces

  The more structure you provide, the more consistent your output becomes.

  The Security Problem

  This topic doesn’t get talked about enough. All of our common bugs have snuck into the training data. SQL injection patterns, XSS vulnerabilities, insecure defaults… they’re all in there.

  The model can’t always be relied upon to build it correctly the first time. Then there’s the question of trust.

  Do you trust your LLM provider?

  Is their primary focus on quality and reliable, consistent output? What guardrails exist before the code reaches you? Is the model specialized for coding, or is it a general-purpose model that happens to write code?

  These are important engineering questions.

  Deterministic Wrappers Around Probabilistic Cores

  The more we can put deterministic wrappers around these probabilistic cores, the more consistent the output will be.

  So, what does this look like in practice?

  Testing is no longer optional. We used to joke that we’d get to testing when we had time. That’s not how it works anymore. Testing is required because it provides feedback to the models. It’s your mechanism for catching problems before they compound.

  Testing is your last line of defense against garbage sneaking into the system.

  AI-assisted review is essential. The amount of code you can now create has increased dramatically. You need better tools to help you understand all that code. The review step, typically done during a pull request, is now crucial for product development. Not optional. Crucial.

  The models need to review itself, or you need a separate review process that catches what the generating step missed.

  The Takeaway

  We’re in an interesting point in time. These tools can dramatically increase your output, but only if you build the right guardrails around them should we trust the result.

  Structure your prompts. Test everything. Review systematically. Trust but verify.

  The developers who figure out how to add predictability to unpredictable processes are the ones who’ll who will be shipping features instead of shitting out code.

  Jan 22, 2026 / DevOps / AI / Programming

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• Everyone crashing out over OpenCode situation. Why not just use Claude Code (2.1+)? Or you know, there’s AMP. AMP exists too, and it looks equally interesting to me.

  Jan 19, 2026 / AI / Tools / Development

• Claude Code has been working great for me. OpenCode looks interesting, but uh, Opus 4.5 access is necessary for real work. I’m not doing any sketchy workarounds to get it running, and API pricing isn’t appealing either. So for now, OpenCode stays firmly in the “interesting” category.

  Jan 19, 2026 / AI / Tools

• Two Arguments Against AI in Programming (And Why I'm Not Convinced)

  I’ve been thinking about the programmers who are against AI tools, and I think their arguments generally fall into two camps.

  Of course, these are just my observations, so take them with a grain of salt, or you know, tell me I’m a dumbass in the comments.

  The Learning Argument

  The first position is that AI prevents you from learning good software engineering concepts because it does the hard work for you.

  All those battle scars that industry veterans have accumulated over the years aren’t going to be felt by the new breed. For sure, the painful lessons about why you should do something this way and not that way are important to preserve into the future.

  Maybe we’re already seeing anti-patterns slip back into how we build code? I don’t know for sure, its going to require some PHD level research to figure it out.

  To this argument I say, if we haven’t codified the good patterns by now, what the hell have we all been doing? I think we have more good patterns in the public code than there are bad ones.

  So just RELAX! The cream will rise to the top. The glass is half full. We’ll be fine… Which brings me to the next argument.

  The Non-Determinism Argument

  The second position comes from people who’ve dug into how large language models actually work.

  They see that it’s predicting the next token, and they start thinking of it as this fundamentally non-deterministic thing.

  How can we trust software built on predictions? How do we know what’s actually going to happen when everything is based on weights created during training?

  Here’s the thing though: when you’re using a model from a provider, you’re not getting raw output. There’s a whole orchestration layer. There’s guardrails, hallucination filters, mixture of experts approaches, and the thinking features that all work together to let the model double-check its work before responding.

  It’s way more sophisticated than “predict the next word and hope for the best.”

  That said, I understand the discomfort. We’re used to deterministic systems where the same input reliably produces the same output.

  We are are now moving from those type of systems to ones that are probabilistic.

  Let me remind you, math doesn’t care about the differences between a deterministic and a probabilistic system. It just works, and so we¹.

  The Third Argument I’m Skipping

  There’s obviously a third component; the ethical argument about training data, labor displacement, and whether these tools should exist at all.

  I will say this though, it’s too early to make definitive ethical judgments on a tool while we’re still building it, while we’re still discovering what it’s actually useful for.

  Will it all be worth it in the end? We won’t know until the end.

  ---

  1. This “we” I use to mean us as in the human race, but also our software we build. ↩︎

  Jan 18, 2026 / AI / Programming / Software-engineering

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• Claude Cowork: First Impressions (From the Sidelines)

  Claude Cowork released this week, and the concept seems genuinely useful. I think a lot of people are going to love it once they get their hands on it.

  Unfortunately, I haven’t been able to get it working yet. Something’s off with my local environment, and I’m not entirely sure what. Claude Desktop sometimes throws up a warning asking if I want to download Node and I usually say no, but this time I said yes. Whether that’s related to my issues, I honestly don’t know. I did submit a bug report though, so hopefully that helps.

  Here’s the thing that really impresses me: Anthropic noticed a trend and shipped a major beta feature in about 10 days.

  That’s remarkable turnaround for something this substantial. Even if it’s not working perfectly for everyone yet (hi, that’s me), seeing that kind of responsiveness from a company is genuinely exciting.

  I’m confident they’ll get it sorted before it leaves beta. These things take time, and beta means beta.

  I have explored using the CLI agents outside of pure coding workflows and so I think there’s a lot more flexibility there than you might expect.

  For now, I’m watching from the sidelines, waiting for my environment issues to sort themselves out.

  Jan 17, 2026 / AI / Tools / Claude

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• OpenCode | The open source AI coding agent

  OpenCode - The open source coding agent.

  Jan 16, 2026 / AI / Programming / Tools / links / agent / open source / code

• Amp

  Amp is a frontier coding agent that lets you wield the full power of leading models.

  Jan 16, 2026 / AI / Programming / Tools / links / agent / automation / code

• After two days with Beads, my agent-based workflows feel supercharged. I’ve been experimenting with agents for about a month now, and something clicked. Could be the new Claude Code 2.1 upgrade helping too, but the combination is 🚀🚀🚀.

  Jan 15, 2026 / AI / Claude / Workflow

• Spent some time with Google Antigravity today and I think I’m starting to get it. Built a few agents to test it out. The agent manager stuff is genuinely interesting and seems useful. The planning features (spec-driven development) though? Not sold on those yet.

  Jan 15, 2026 / AI / Google / Agents

• Beads: Git-Native Issue Tracking for AI-Assisted Development

  If you’re working with AI coding agents like Claude Code, you’ve probably noticed a friction point: context.

  Every time you start a new session, you’re rebuilding mental state. What was I working on? What’s blocked? What’s next?

  I’ve been using Beads, and it’s changed how I manage work across multiple AI sessions.

  What Makes Beads Different?

  Beads takes a fundamentally different approach. Issues live in your repo as a .beads/issues.jsonl file, syncing like any other code. This means:

  • No context switching: Your AI agent can read and update issues without leaving the terminal
  • Always in sync: Issues travel with your branch and merge with your code
  • Works offline: No internet required, just git
  • Branch-aware: Issues can follow your branch workflow naturally

  The CLI-first design is what makes it click with AI coding agents. When I’m working with Claude Code, I can say “check what’s ready to work on” and it runs bd ready to find unblocked issues. No copying and pasting from a browser tab.

  Getting Started

  Getting up and running takes about 30 seconds:

  # Install Beads
  curl -sSL https://raw.githubusercontent.com/steveyegge/beads/main/scripts/install.sh | bash
  
  # Initialize in your repo
  bd init
  
  # Create your first issue
  bd create --title="Try out Beads" --type=task
  

  From there, the workflow is straightforward:

  • bd ready shows issues with no blockers
  • bd update <id> --status=in_progress to claim work
  • bd close <id> when you’re done
  • bd sync to commit beads changes

  Why This Matters for AI Workflows

  The real power shows up when you’re juggling multiple tasks across sessions. Your AI agent can:

  1. Pick up exactly where you left off by reading the issue state
  2. Track dependencies between tasks (this issue blocks that one)
  3. Create new issues for discovered work without breaking flow
  4. Close completed work and update status in real-time

  I’ve found this especially useful for longer projects where I’m bouncing between features, bugs, and cleanup tasks. The AI doesn’t lose track because the state is right there in the repo.

  Is It Right for You?

  Beads isn’t trying to replace GitHub Issues for team collaboration or complex project management.

  It’s designed for a specific workflow: developers using AI coding agents who want persistent, agent-friendly task tracking.

  If you’re already working with Claude Code, Aider, or similar tools, give it a try. The setup cost is minimal, and you might find it solves a problem you didn’t realize you had.

  Jan 14, 2026 / Productivity / AI / Developer-tools / Git

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