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The issue was not the tools. It was opening five of them before deciding what the log file was for. The log file was already on the screen. A remote Windows workstation had failed a desktop build, and the relevant file was sitting in a local app directory, something like: C:\Users\<user>\AppData\Local\<app>\logs\build.log The remote session was working. The error was visible. The next step seemed small: get the log back to the local laptop, open it in a familiar editor, compare it with the issue notes, and pull out the part that mattered. That should have been a 30-second task. Instead, it turned into five context switches. The first context was the remote session The remote desktop session made sense. The build failed on that machine, the app was installed there, and the log path was easier to find visually than by guessing from memory. So far, nothing was wrong. The file was selected. The timestamp matched the failed run. The log looked useful. It probably had the stack trace, the missing dependency path, or the configuration mismatch that explained the build failure. Then came the small but surprisingly annoying question: How should this file leave the remote machine? That is where the workflow started to wobble. The second context was chat The first instinct in many teams is chat. Drop the file into a message to yourself, a teammate, or the debugging thread. It is fast, already open, and keeps the file near the conversation. For some files, that is the right move. A screenshot, a short error snippet, or a quick “does this look familiar?” artifact belongs naturally in the discussion. But a full log file is not always a chat artifact. If it goes into chat, will anyone know later whether it was the first failing run or the second? Will it be obvious which remote machine produced it? Will the file still be easy to find after the thread moves on? Chat was not wrong. It was just not clearly the right home for this specific file. So the workflow moved on. The third con

With increased adoption of AI, there is often an argument that code-reviews are now the new bottleneck. And I agree with this completely. Code-Reviews, especially the review you do yourself after AI has written your code, take time. But I would object to the notion that this is a bad thing. What is a bottleneck? A bottleneck is something that slows down the process. It becomes a point where work must get in a line, to pass through a narrow space. With the speed of AI producing code, code reviews become a bottleneck. But is having a bottleneck in the process always a bad thing? The value of slowing down I can only speak from my personal experience of developing software for roughly 7 years now. But in my experience, slowing down is not always bad. On the contrary, it can be very healthy. When you slow down, and take the time to really think about things, you often come up with insights that you would not have if you always rush through things. And these insights can be golden opportunities to change something for the better. Be that a subtle bug discovered, be that a design flaw addressed or something else - the list is long. But as British computer scientist Tony Hoare famously said: "There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies." But simplicity is hard "I would have written a shorter letter, but did not have the time." If it was Mark Twain or Blaise Pascal who said it is beside the point. The point is, there is a lot of truth in this quote. A writer of prose I know also confirmed what many senior software engineers know - to make something complex simple and easily comprehensible takes way more time and effort in the form of careful thought than it takes to leave it being complicated and hard to understand. AI is good at writing code quickly, yes. But is it also good at writing code which has high q

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The Dell 14S represents the new normal of laptop pricing, but it has the quality to back up its cost.

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ChatGPT Work is an agent that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work.

For a long time, education and work rewarded one thing above all else: the ability to produce correct answers. School exams were built around it. Technical interviews were built around it. Even many engineering jobs were built around it. The person who could respond faster, explain better, and deliver the right output was often seen as the most valuable person in the room. But AI is changing that. Today, answers are becoming cheap. With modern AI tools, anyone can generate code, summaries, documentation, architecture drafts, and even product ideas in seconds. The scarcity is no longer in producing answers. The scarcity is in defining the right problem. That is why, in the AI era, learning how to ask better questions matters more than learning how to write better answers. The Bottleneck Has Moved The biggest shift is not that AI can answer questions. The bigger shift is that answering is no longer the hardest part. When answers can be generated instantly, the real bottleneck becomes: What exactly should be asked? What is the real problem behind the surface request? What constraints actually matter? What outcome is considered good enough? AI can generate many possible answers. But it still depends heavily on the quality of the question. A vague prompt creates vague output. A precise question creates leverage. In that sense, the person who defines the problem is now more important than the person who simply responds to it. The Problem Setter Is More Valuable Than the Problem Solver This idea may sound exaggerated at first, but it becomes obvious in practice. Suppose someone says: Optimize this system. That sounds like a reasonable task, but it is actually too weak to produce a strong result. Optimize for what? Cost? Latency? Reliability? Simplicity? Team productivity? Now compare it with this: We have a Node.js API running on AWS ECS. Under burst traffic, CPU throttling causes latency spikes. How can we reduce p95 latency without increasing infrastructure cost by more

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Some of you may have noticed I disappeared a bit from the community over the last couple of weeks....

I have been building software for sixteen years. I have four ambassador titles I earned honestly. And last week I sat at my desk at eleven at night, certain that everyone else my age was further ahead than me. You know that feeling. The one where you scroll past someone's launch, someone's promotion, someone's clean little success, and a cold voice says you should be there by now. It does not care what you have done. It only points at what you have not. For most of my career I treated that voice as a problem to solve. If I could learn one more tool, ship one more thing, earn one more title, it would finally go quiet. So I did. I learned the tools. I shipped the things. I earned the titles. The voice did not go quiet. It moved the finish line and waited for me there. Here is the opinion I wish someone had handed me a decade ago. Feeling behind is not a bug in you. It is the tax you pay for caring about the work. The people who feel the most behind are almost never the ones who are actually behind. They are the ones paying attention. They see the gap between what they made and what they meant to make, and that gap never closes, because the moment you get better, your taste gets better too. The gap is not evidence that you are failing. The gap is proof that you still have standards. I know engineers with twenty years and a wall of real accomplishments who quietly feel like frauds. I know brilliant people five years in, staring at a job market that feels brutal, convinced everyone else got a memo they missed. None of them are behind. All of them are exhausted from running a race that has no finish line, on a track only they can see. The comparison is rigged, and it is worth saying why. You compare your inside to everyone else's outside. You know your own doubt, your own half-finished drafts, your own two in the morning. You see their launch, their title, their highlight. You are matching your bloopers against their trailer, and then calling yourself slow. So what change

A while back I was researching a topic I didn't know much about — the kind of casual, late-night "let me just ask the AI a few questions" session. A few messages in, I asked a follow-up that only made sense in the context of what we'd just been talking about. I didn't restate the subject, because... why would I? We were three messages into the same conversation. The answer came back completely off-topic. It had lost track of what "it" referred to, latched onto the wrong noun, and confidently explained something I hadn't asked about at all. Not a small tangent — a whole paragraph about the wrong thing. My first reaction was annoyance at the model. My second, more useful reaction came a bit later: I'd been treating it like a person who remembers what we were just discussing and fills in the gaps naturally. It doesn't do that the way a human conversation partner does. If I don't restate the subject, it's genuinely not there for the model — it's not being lazy, there's just nothing to work with. So I started over-specifying. Every follow-up got longer: restate the subject, restate what I actually wanted, restate the constraint I cared about. It worked, but some days I didn't have the energy for it — I'd just take the mediocre answer, say "ok thanks," and move on. Which meant I was quietly leaving useful answers on the table half the time, just because typing out the full context felt like a chore. Eventually I stopped thinking of it as "the AI being difficult" and started treating it as a simple rule: if I want it to know something, I have to say it. It won't infer the unstated stuff the way a person would , no matter how obvious it feels to me. Once that clicked, a few concrete habits followed. Restate the subject, every time Not "what about the second one" — the actual name of the thing. It costs three words and removes an entire failure mode. Say what you actually want, not just the topic "Tell me about X" and "I'm trying to decide whether X is worth the switching co

Everyone talks about the build. Nobody talks about what happens the week after, when you go to actually tell people it exists and discover every distribution channel has its own quiet gatekeeping you didn't know about until you hit it. Hacker News flagged my Show HN before it ever reached the front page. Not rejected — flagged, silently, likely because the account posting it was brand new with a self-promotional link and zero history. No warning, no explanation, just gone from /newest for anyone not specifically looking. Reddit was worse in a different way. r/webdev's AutoMod rejects any submission from an account under three months old with low karma — a hard gate, not a soft one, and it doesn't care which day you post or how you phrase it. r/SideProject let the post through technically, but Reddit's own spam filter quietly removed it minutes later, invisible to everyone except me looking at my own profile. X was just silence. Zero followers means the algorithm has no graph to push the post into. Four views, three of which were probably me refreshing. The one channel that actually worked was the one with the lowest bar to entry: writing. dev.to doesn't gate you behind account age or karma. You write something, it's live, and if it's genuinely useful, people find it — slowly, but for real. That's where actual engagement happened. The pattern underneath all of this: almost every high-leverage distribution channel is, by design, hostile to accounts with no history. That's not a bug — it's the exact mechanism that keeps those platforms usable, and it exists specifically to stop people doing exactly what I was trying to do: show up once with a link and leave. The system is working as intended. It just doesn't feel that way when you're the one hitting the wall. What's actually working, three weeks in, isn't a growth hack — it's writing things people search for, verbatim, and being patient about everything else building account history the boring way: showing up, commenti

Shared tool configurations drift when developers run local agents. Adding a new MCP server to a team setup usually fails to reach existing local configurations, leaving developers with outdated toolsets. We resolved this in our latest CLI release by introducing stateful configuration back-filling. The update merges new servers into local environments while preserving custom developer adjustments. What's new Angular stack integration : Added frontend domain detection for angular.json and @angular/* packages in the /stack-set command. The oma-frontend skill now includes angular-rules.md to enforce standalone components, OnPush change detection, and signals. API evolution patterns : Added API lifecycle patterns based on the MAP framework to oma-architecture . This includes Sajaniemi's 11 variable-role taxonomy to guide naming rules in oma-refactor . Windows scheduling updates : The schtasks adapter now maps weekly cron ranges like 1-5 or lists like 1,3,5 directly to Windows task scheduler formats. Model validation : Added vendor validation to the schedule:add command. The CLI now rejects unknown models at registration time rather than failing during execution. Keeping local environments synchronized across diverse OS targets requires strict validation. These fixes ensure configuration changes flow correctly without disrupting developer-specific settings. What's fixed MCP server synchronization : Fixed an issue where SSOT servers added to .agents/mcp.json were only copied if .mcp.json was entirely absent. The CLI now reads the source of truth on every run and merges missing entries. Test execution reliability : Restructured the project root resolution tests to mock the filesystem walk. This isolates test runs from ambient files on CI runners and avoids false failures. Market diversity flags : Corrected the --diversity-threshold flag documentation to reflect that the default threshold is not enforced unless the flag is explicitly set. Cleaning up obsolete protocols reduc

I just submitted another Chrome extension to the Chrome Web Store. I have submitted multiple extensions overtime. Mostly for my own tooling and community share or just because idea was fun. The first time took 3 attempts. The second time I got rejected in 12 hours for something completely avoidable. Here's every gotcha I hit — so you don't have to. 1. Manifest description has a 132-character hard limit Not documented prominently anywhere. You'll get a cryptic upload error: "The description field in manifest is too long." Your package.json description or wxt.config.ts description gets baked into manifest.json — check it BEFORE you zip. Fix : Count characters. 132 max. Put the detailed description in the CWS form, not the manifest. 2. Don't put a "Keywords:" line in your description I literally had: Keywords: pinterest seo, pin score, pin quality, pinterest optimizer... Rejected within 12 hours for "Keyword Spam." CWS explicitly bans keyword lists in descriptions — even if they're relevant. Your keywords should be woven naturally into prose. Fix : Write human sentences that include your keywords. "Score your Pinterest pin quality before publishing" contains 3 keywords naturally. 3. upload-artifact@v4 silently skips hidden directories If your build tool outputs to .output/ (like WXT does), GitHub Actions' upload-artifact won't find it. The glob path: .output/*.zip returns nothing because .output starts with a dot. Fix : Add include-hidden-files: true to your upload-artifact step. - uses : actions/upload-artifact@v4 with : path : .output/*.zip include-hidden-files : true 4. optional_permissions need justification too I added sidePanel as an optional permission (reserved for a future feature). CWS asked me to justify it. Optional doesn't mean invisible to reviewers. Fix : Add a justification for EVERY permission — required AND optional. Explain what it'll do and why it's optional. 5. "Support URL" is not your email address The form has separate fields: Support email : yo

The MVP was a great idea that got misused. "Minimum viable product" was meant to be the smallest experiment that tests a hypothesis. In practice it became an excuse to ship something broken and call it strategy. The minimum lovable product — MLP — is the correction: the smallest release that people actually want to use, not just tolerate. Knowing which one you need is a scoping decision, not a philosophy. The difference in one line An MVP asks will they use it at all? An MLP asks will they love the part we built? The MVP tests demand with the roughest possible artifact. The MLP narrows scope but polishes what remains until it's genuinely good. Both are about doing less. They disagree on where the "less" goes — fewer features versus rougher features. Why the bar has risen When users had few alternatives, a rough MVP could win on novelty. Today almost every category is crowded, and people judge a new product against the polished tools they already use. A janky first impression doesn't read as "early" — it reads as "not for me," and they don't come back. In a saturated market, lovability is the viability test. When an MVP is still right Ship a true MVP when the core question is demand, not quality: You're genuinely unsure anyone wants this at all. The audience is early adopters who tolerate rough edges for access. You can learn what you need from a small, forgiving group. Speed to a signal matters more than the strength of the signal. Here, spending weeks polishing something nobody wants is the expensive mistake. When to reach for an MLP Choose an MLP when demand is fairly clear but the market is competitive: Users have real alternatives and will compare you to them. Your differentiation is the experience — feel, speed, design. First impressions are hard to reverse. Word of mouth depends on delight, not just function. Scope narrow, finish deep The trap with "lovable" is treating it as license to add features. It's the opposite. Pick fewer things and finish them complet

I've been coding with AI agents for about two years. Every major one. Cursor, Copilot, Codex, OpenCode. They're good at generating code. They all share one problem. They forget everything. You finish a session, close the window, and the agent resets. Next time you open it, you're starting from zero. "We use pnpm, not npm." "The database is SQLite, not Postgres." "Don't touch the migrations folder." You repeat yourself. Every. Single. Time. Some tools added memory features. Usually as an afterthought. A pinned file. A custom instruction. A context window that grows until it hits a wall and everything old gets silently dropped. I didn't want a bigger context window. I wanted an agent that accumulates knowledge the way a colleague does. Not by being retrained. By taking notes, writing down what it learned, and reading those notes next time. That's what Jean2 can do. Not through fine-tuning. Not through vector embeddings. Through files on disk that the agent reads and writes itself. But here's the thing: none of this is on by default. By default, Jean2 is as bare as Codex or OpenCode. A blank prompt. No memory. No skills. No session search. You opt in to each layer in workspace settings . That's the point. You build the agent you want, layer by layer. The Four Layers If you turn them on, Jean2's agent has four knowledge layers that persist across sessions. They're not features bolted on top. They're part of the system prompt that gets composed every time a session starts. 1. Workspace Memory Turn on workspace memory in workspace settings , and the workspace gets two files: MEMORY.md for shared knowledge and USER.md for your personal preferences within that workspace. Both live at <workspace>/.jean2/ . The concept is simple. Shared knowledge that's useful for any agent working in that workspace. "We use pnpm." "The database is SQLite." "Don't touch the migrations folder." Whatever agent you bring in, coding specialist, reviewer, docs writer, they all get the same context

Open-source workspace for AI agents and workflows Discussion | Link