标签：#Product

Your personal context file for every agent. Discussion | Link

Free browser tools for everyday tasks, no login Discussion | Link

I’ve been out of context with my computer science degree for years, and honestly, it feels overwhelming. I want to change that, so I'm starting this log to document my comeback story, my failures, and my progress. What I did today: Acknowledged the starting line. Took 10 minutes to think about where I left off and what I need to tackle first. Decided to write daily logs. This is not my first attempt to finish the degree, but hopefully with some public eyes on it, it will be the last. 10 subject left. Starting with Mathematical analysis. The biggest challenge right now: Feeling completely out of context and fighting the urge to get distracted by everything else. Many things have changed since i was actively going to classes. My generation graduated many years ago and i feel like getting all the information I need will be extremely difficult since i don't know anyone. Getting information from the university websites and emailing professors will be slow. Next steps: Tomorrow, I'm going to try to overcome my overdramatic anxiety and post in the student groups I was in, in hope that somebody might have new information about the subjects. I will go start lesson #1. Goal is to ease into the material and start a habit, rather than pressuring myself to do everything at once. Come back tomorrow.

Harness local AI for notes Discussion | Link

I don’t log in to my LinkedIn account anymore, haven’t done so in years. “AI first this, AI driven that,” alright, alright, I get it! You guys absolutely love AI, and I can’t stand the fact that after almost 5(!!!) years, people still talk about it like it’s the best thing since chicken nuggets. Back when LLMs have started showing somewhat positive results when it came to generating less-than-average code, I came to a devastating conclusion: My friends and colleagues might not actually enjoy coding at all. Maybe they just didn’t enjoy coding the way I do—but either way, I was extremely sad and actively burnt out to find that everybody around me jumped on the prompting bandwagon without giving it much of a critical second thought. I could argue that it was partially because of their employers, but it was definitely because they chose and wanted to. They pressed me “It’s the future, aob2f, we don’t need to write code anymore.” “I just tell Claude to do it, and it’s done.” “I don’t write a single line of code anymore, don’t be left behind.” Some even outrageously claimed “Yeah I reviewed Claude’s 40k lines PR in two days, it was good.” I was baffled. Was I in this sick, absent-minded Truman show spinoff? Were these the same smart, even genius individuals that have built and driven the world’s innovation in the past 30 years with their own hands and minds? It got to a point where I started doubting myself. Maybe it’s really that good? I genuinely gave it a thought. Eventually, I reluctantly gave in and tried vibe coding for the first time… You see, up until then I exclusively used AI as a sophisticated search engine. I asked a technical question—got an answer. More than 50% of the times it was inaccurate or a full-on hallucination, and then I validated the result through trial and error. Admittedly it was better than blindly googling niche bugs and finding nothing but a lonely and vague question from 8 years ago on Stack Overflow. But matter of fact—vibe coding was a mi

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I watched an AI agent send 200 emails to the wrong recipients because I forgot one validation check. The emails were well written. The offers were real. The recipients were just... not our leads. That was early. I learned fast. Every agent I build now has three layers of guardrails before it touches a database or an API. Here's exactly what those layers look like and why they're non-negotiable for production. Input Validation: Your Prompt Is Not a Schema The first mistake people make is trusting the LLM to produce valid output. It won't. Not reliably. I've seen GPT-4 return a JSON key called "emial" instead of "email" in a critical pipeline. One typo, and the whole record is garbage. The fix is a strict validation layer that runs before any data reaches your system. In my AI resume tailor, I use a JSON schema with conditional presence flags. Every field that must be real has a has_* boolean guard. If the LLM tries to fabricate a phone number, the schema rejects it. const resumeSchema = z . object ({ contact : z . object ({ email : z . string (). email (), phone : z . string (). optional (), has_phone : z . boolean () }). refine ( data => { // If phone is present, the guard must be true return data . phone ? data . has_phone : ! data . has_phone }, " Phone number present but has_phone flag is false " ) }) This pattern catches hallucinations before they corrupt your database. The schema is the contract. The LLM is just a suggestion engine. Permission Scoping: Give Agents the Minimum They Need An agent should never have write access to tables it doesn't need. That sounds obvious, but I've seen production systems where a job description rewriting agent had full CRUD access to the user table. When I built the LLM scoring pipeline for a job board platform, I created separate database roles. The scoring agent only had SELECT on the job listings table and INSERT on a scoring results table. It never touched users, applications, or configuration. Even if the prompt was hijack

Originally published on hexisteme notes . I write these engineering notes with one main model, and until recently I also reviewed them with that same model. Same family writes, same family checks its own work. That sounded fine right up until I had ten queued posts sitting in a publish backlog and a nagging thought: if the writer and the reviewer come from the same training distribution, what exactly is the review checking for? So I ran an experiment. I took the queue and had a different vendor's model audit it before anything went out — not to replace my own review, but to see what a genuinely different set of weights would flag that mine hadn't. The setup: copies only, and a self-verifying prompt The mechanics were deliberately boring. I copied the ten queued articles into a scratchpad directory and exposed only that copy to the auditor via --add-dir — the auditor never got write access to the originals, so nothing it did could touch the source of truth by accident. The audit itself ran as agy --model gemini-3.1-pro-high , pointed at the copy directory, with one instruction: find technical factual errors, broken sentences, cross-article inconsistencies, unsupported claims, and tone violations, and verify each one yourself on the web before reporting it. I wanted a model that would check its own homework, not just pattern-match on "this looks wrong." It came back with seven findings. Rule one: don't trust the auditor either Seven findings from a different vendor is not the same thing as seven confirmed bugs. I re-verified every single one independently — grepping the original text, checking official documentation, and where possible checking against a real machine — before touching anything. Of the seven, six held up and got fixed. One didn't: the auditor flagged a sentence as an error, and when I went back to the primary source, it turned out to be the auditor misreading a perfectly correct sentence, not a defect in the writing. Without the re-verification step, I

Keep your desktop clean by auto-organizing screenshots. Discussion | Link

When I first wrote about NutriAgent in November 2025, it was a full application. It had a Python backend, a web interface, a Telegram bot, user accounts, Google OAuth, Supabase, and a Google Sheets integration. Recently, I reproduced its core workflow as a skill for my personal AI agent. It took around 15 minutes and two prompts. I didn't build another backend, deploy a service, or implement OAuth again. I explained how I wanted the workflow to behave, tested it, and watched a new row appear in my nutrition spreadsheet. The original application wasn't a mistake. It was how I could deliver that experience with the tools available at the time. Eight months later, the starting point had changed. Eight Months Ago, This Was an App I built NutriAgent because I wanted to track calories and protein without trapping my data inside a nutrition app. I wanted the raw records in a spreadsheet I controlled, where I could create my own reports and eventually connect nutrition with my training data. The first version was a personal n8n workflow. It worked for me, but when a friend wanted to try it, I realized that everything was tied to my accounts. To make it reusable, I rebuilt it in Python and added the parts a real multi-user product needed: authentication, storage, a web interface, Telegram, Google OAuth, conversation history, and account linking. I've already told that story in I Ditched MyFitnessPal and Built an AI Agent to Track My Food , and later wrote about what broke after I used it every day for a month . This article starts after that version. My Gaming PC Became an Agent Box I had a modest gaming PC at home with 16 GB of RAM and a 1 TB drive. Using it through Windows, WSL, and remote desktop from my Mac felt awkward, so I installed Linux and turned it into a remote box for running agents. I'll write about that setup separately. I moved Hermes there from a VPS. Hermes is the personal agent I now run on that machine. It can load reusable skills and use tools connected

A terminal, a real browser, and Claude Code under one roof Discussion | Link

Originally published at boldpilot.club Most startup launches fail not because the product is wrong, but because the planning collapses under its own ambiguity. A solid launch plan template for startups gives you a structure that converts vague intentions — "we'll do some PR, post on social, maybe a Product Hunt thing" — into sequenced tasks with owners and deadlines. If you're building something and your launch plan currently lives as a notes app dump or a half-filled Notion page with seventeen unticked checkboxes, that's the problem this article addresses directly. A usable launch template has five core components: a pre-launch timeline, a channel priority stack, a messaging framework, a metrics baseline, and a post-launch review cadence. Everything else is optional decoration. Why Most Launch Plan Templates Get Abandoned The templates people download from Google — the ones with seventeen tabs and color-coded Gantt charts — get opened once and then quietly archived. I've seen this happen with teams that had more than enough time and resources. The issue is that those templates are designed to look thorough, not to be used under the actual pressure of a launch week. According to CB Insights, 35% of startup failures cite "no market need" as the primary cause, but the second and third most common reasons — running out of cash and not having the right team — are both downstream of poor planning and sequencing. A launch plan that nobody opens doesn't prevent any of those outcomes. The templates that do get used share one quality: they're shallow enough to skim at 11pm the night before a deadline. One page if possible. Two at most. If your template requires a tutorial to operate, it's already failed. I'd push back on the common advice to "start with goals." Goals are fine, but most founders already know what they want — signups, revenue, waitlist growth. What they're missing is the sequencing. Start with the constraint instead: what's your actual launch date, and what ab

Every team doc tool these days is "searchable." Notion, Confluence, wikis — you can find any page in seconds. But search only tells you a document exists; it doesn't tell you how it connects to the five other docs that explain why it looks the way it does. New hires still end up pinging three people on Slack to reconstruct the reasoning behind a decision that's technically "documented" somewhere. This post is about the difference between a searchable knowledge base and a connected one — and why most teams have the first but assume they have the second.

The AI book service that shows its work Discussion | Link

I used to deride the idea of an AI assistant from the moment they entered the picture (after seeing all the different *Claw variants). Why would regular people like me need an assistant? The best use case I had heard was: "Oh! It helps us decide whether I or my partner should drive the kids today!" Solving that sounded like a silly problem for an AI assistant to handle. Again, I didn't know any better because I didn't have that problem. I thought I could handle one-off tasks with just an AI subscription. What else was there? I only found the answer once I had a specific use case for it. I attended a business event, talked to a dozen people, and collected several business cards. I wanted to send each person a personalized email thanking them and continuing our conversation. If I were to do this manually, the process would look like this: open the email client, manually type in each email address from the business cards, ensure I typed everything correctly, compose my message, and again, make sure I didn't press "send" prematurely. Just thinking about it felt tedious. That is when the idea of an assistant started making sense. I fire up my coding agent(not a *Claw still), I take a single photo of all the business cards together. I ask the it to extract the names and email addresses. Then, I ask it to loop through the list and ask me what I want to send to each person. It creates drafts(which I still manually review - can't trust them enough), I say send, and then it sends them all automatically. It feels exactly like talking to a real assistant: you tell them what you want done, and it gets done without you having to press buttons or navigate a UI. That is exactly what I did; I simply gave it instructions using my voice. This makes me feel that having an AI assistant is indeed helpful. It might have also been useful to jump on this a little sooner, as I could have bought that Mac Mini at the older, lower price.

Every AI-generated site we have inherited was missing the same eighteen things. None of them are visible in a screenshot. All of them are visible to Google. July 10, 2026 An AI-generated site looks done. It has a hero, sections, a color palette, and copy that reads well in a screenshot. Then we open the page source, and the production work is missing. Not some of it. The same eighteen things, every time. None of them change what a human sees in a browser. All of them change what a crawler, a link preview, or a cache does with the page. Here is the list we run before we call anything live. Crawlability and indexing This is where the gap is widest, because a client-rendered single-page app hands crawlers an empty div and expects them to run JavaScript to fill it. Many will not. We fix that with static work. Prerendered static HTML per route , so the first paint is real content and not a loading spinner. A sitemap.xml generated from a single route manifest , so it lists every page and no page twice. A robots.txt that points at that sitemap and does not accidentally disallow the whole site. A canonical URL on every page , because a screenshot cannot show you a missing canonical tag. A meta title and description written per page , not one template repeated across the whole site. Structured data as JSON-LD for the page types that support it. IndexNow submission on deploy , so search engines learn about changes without waiting for a crawl. An llms.txt file describing the site for the AI crawlers that now read it. Sharing and presentation A link is content too. When someone pastes the URL into Slack or iMessage, the site is representing itself, and the defaults are usually blank. Open Graph tags for the title, description, and image. Twitter card tags , which are close to Open Graph but not identical. A per-page share image at 1200x630 in PNG. WebP renders unreliably in LinkedIn and iMessage previews, so we ship PNG here even though we prefer WebP elsewhere. Descriptive alt

In this blog post, we will see why almost every major AI subscription, ChatGPT, Claude, Perplexity, and Gemini, somehow landed on the exact same $20 a month price tag. We will trace it back to where it started, look at the actual reasoning behind the number, and figure out whether this price ceiling will hold or eventually crack the way streaming subscriptions did. The $20 monthly price point shared by ChatGPT Plus, Claude Pro, Perplexity Pro, and Google AI Pro traces back to OpenAI's February 2023 launch, which was designed to subsidize free-tier costs rather than reflect the actual value of the product. Competitors adopted the number through price anchoring, not independent cost analysis. The same pattern has extended to smaller AI tools and is now repeating at higher tiers, with $200 and $100 monthly plans emerging for power users. Despite identical pricing, what each $20 subscription delivers varies significantly across providers in terms of usage limits, features, and model access. The Coincidence That Isn't a Coincidence As of mid-2026, ChatGPT Plus, Claude Pro, and Perplexity Pro all cost exactly $20 a month. Google AI Pro (formerly Gemini Advanced) sits one cent below at $19.99. Four completely different companies, four completely different models, and yet the sticker price converges on almost the same number. That's not four companies independently landing on the same cost math. It's one company setting a price, and everyone else deciding not to compete on it. Where It Actually Started: OpenAI, February 2023 ChatGPT launched free in November 2022 and crossed a million users within about a month, which was an enormous number for a research preview. On February 1, 2023, OpenAI introduced ChatGPT Plus at $20 a month, expanding it internationally on February 10. The pitch at the time was simple: general access even during peak load, faster responses, and priority access to new features. Worth remembering: this was the GPT-3.5 era. GPT-4 hadn't shipped yet. Subs

Windows clipboard history that searches inside screenshots Discussion | Link

A free visual story development workspace Discussion | Link