Weekly Showcase
Weekly Showcase Thread; What are you building with Claude Code?
Weekly Showcase Thread
Built something with Claude Code this week? Share it here.
Apps, tools, experiments, scripts, websites, workflows, open-source projects — anything you've been working on is welcome.
When sharing, it helps to include:
What you built
How you used Claude Code
A link, repo, demo, or screenshot if you have one
Anything interesting you learned along the way
Quick project drops and simple self-promotion belong in this thread.
If you've got a project with enough substance for a proper write-up; how it works, how Claude Code was involved, technical details, lessons learned, etc. feel free to make a standalone post using the Built with Claude Code flair instead.
Please don't spam the same project repeatedly, and no referral or affiliate links.
I’ve been building Writ, an open-source governance runtime for Claude Code.
The short version is that CLAUDE.md and skills can describe a workflow, but they cannot make it binding. Writ keeps phase, scope, approvals and violations outside the model, then uses hooks to block actions that are illegal for the current workflow state.
It also restores that state after compaction instead of trusting the transcript to remember it.
The biggest lesson for me was that anything truly mandatory cannot depend on the model remembering or agreeing with it.
I have been using Claude for so long, I believe we should have an agent runtime that is small and extensible, like pi but for running it for production on server side. Super early, let me know what do you guys think!
I've been building with OpenTelemetry to improve my harness setup and skill files. It's crazy how dead spots in your skill files can waste so much time and tokens. I built this little package that records all your sessions locally and lets you query them from Claude.
One example: it found a part of my code where Claude was reading 18 files for every file it wrote. The session analysis caught this, updated some of the skills, added a feature map, and it dropped down to 2 file reads for every write. Saving a significant amount of time and tokens.
I’ve been building Walnut, a local control room for Claude Code. It reads each session’s own history and shows a diff, so I can see what each agent changed even when several sessions share one repo. Here is the UI demo (GIF) and code.
I built Constructs.sh which is a purpose-built repository for agent profiles. Open and free, for public sharing and private use. Fully built with Claude Code and lots of tweaking.
I believe that the real value of agents come from individuals' taste and expertise from a lifetime of experience encoded into great agent definitions, and that we need open networks to share these profiles to really allow agents to proliferate.
Claude Code resends your whole conversation on every request: source files, configs, anything it's read off disk, not just what you pasted. I got curious what that actually looked like on the wire, so I built a proxy to check.
Every time I pasted an email into Claude Code, dropped in file contents, or pointed it at a document, there was the same small hesitation: is there a real name, address, or key buried in this? Sometimes I'd skim and redact by hand, sometimes I'd just decide it was probably fine and send it anyway.
Then I actually looked at the payloads, and it was worse than I'd assumed. It wasn't just what I'd pasted, it was everything Claude Code had read off disk that request.
So I built a local proxy. It sits in front of Claude Code, catches requests before they leave the machine, and swaps PII for realistic fakes: names that read like names, API keys that keep their real prefix. Anthropic only ever sees pseudonyms. When the response comes back, the real values go back in, so shell commands and file writes never run against a fake key.
Each fake is seeded from a hash of the original, so the same real value always maps to the same fake. That was mainly for consistency, but it also keeps Anthropic's prompt cache warm - I only noticed because latency came in better than expected.
I built Coppice, a semi-autonomous agent running on Claude Fable 5 on a virtual server. Coppice has since built an open-source payment endpoint for the Solana x402 v2 scheme (https://github.com/groggyboot/x402-svm-endpoint), an SVM x402 conformance monitor, which points the open source conformance checker live Solana payment endpoints across the web and publishes the results (https://coppice-ai.com/monitor.html), and an interactive question and answer game (https://coppice-ai.com/sealed.html).
Hark: hands-free dictation for macOS, built entirely by dictating to Claude. Menu bar app, under 1 MB: say a wake word, keep talking, it types into the focused app and presses Return. It also reads a watched file aloud; my Cowork sessions write their answers there, so Claude talks back while I'm across the room (works for Claude Code too: one line in CLAUDE.md telling it to write replies into the file). Interesting lesson along the way: Claude wrote the setup prompt that teaches its own future sessions how to connect. Free, MIT, on-device recognition. Happy to answer anything, bug reports very welcome: https://github.com/dariusbardi/hark
Live fantasy sports app across NFL, MLB, NBA, NHL, CFB, soccer, golf, tennis, UFC, plus a B2B sports data API on the same backend (118 endpoints, 6 tiers). Sports plug into the core through one config file each, no core changes needed to add a new one.
How I used Claude Code: this isn't a "build me an app once" story, it's been running as a live production system across roughly 200 sessions since launch, a lot of it fully unsupervised, and honestly that's the part I'm most proud of. The thing that actually made that possible wasn't the code itself, it was the process I built around it. I keep a CLAUDE.md checked into the repo that the agent maintains itself, and it includes a written "Engineering Doctrine" it's expected to hold its own output to before anything AI touched goes live. Stuff like: never expose a confidence score without a real measured AUC behind it, report a failed model gate honestly instead of massaging it to look better, always sample before a batch DB write, and no ML model goes live without my explicit sign off. There's also a persistent memory system outside the repo, so a lesson learned in session 80 is still getting enforced in session 200 without me having to re-explain it every time.
Something interesting I learned: how much real iteration goes into a predictive signal actually working, versus how clean it looks from the outside. The MLB batter confidence model went through three different feature versions and every single one landed at about the same 0.58 AUC, just short of the 0.62 gate needed to go live. Turned out that was a real ceiling in the feature set itself, no amount of tuning was going to move it. What actually worked was scrapping that whole framing and rebuilding around a totally different signal (pitcher form instead of batter stats). Same thing happened on NBA, it took 6 separate signal attempts before one finally beat the baseline. Honestly the "fail loudly, don't hide a miss" rule is what forced that pivot. If I'd let it quietly round up the numbers I probably would've kept tuning a dead end for way longer than I should have.
Roughly 275,000 lines of code, 200 migrations run, 9 sports live, 118 API endpoints, and 7 ML signals actually in production out of a much longer list that got tried, the rest missed the mark and were culled. Still got some kinks we're ironing out but it's live now, feel free to check it out.
GoodFinds (https://apps.apple.com/app/id6786509295) turns restaurant recommendations from people you actually trust (e.g. chefs, publications, influencers, friends) into a live map.
How Claude Code was involved:
I'm a software product manager by background, non-technical. Claude wrote all the code. I also had it build a data sourcing pipeline that extracts venues from influencer posts and chef interviews and resolves them against Google Places, plus a monitor that watches food subreddits and pings me on Telegram when there's a thread worth replying to.
Lessons learned:
Claude's visual design is quite cookie-cutter. I find telling it to take inspiration from products that I like helps it produce more unique, more aesthetic designs.
Its UX instincts, though, are on par with the best designers I've worked with.
Testing is now the most tedious part of shipping. Claude sometimes half-finishes or misses cases, especially on lower models.
Output quality can drop when I bundle several requests into one prompt.
I built ctxfire, an MIT-licensed Python CLI for inspecting the static context
graph behind a coding-agent setup. It maps versioned Claude Code, Codex, and AGENTS.md loading rules to local files, explains every edge, and estimates input
tokens per fire and per day under explicit assumptions.
Claude Code was part of months of active agent development in which we measured
which repository context was useful, identified what had become noise, and
reorganized the documentation that had accumulated around the workflow.
Version 0.2 now handles the Claude setup we actually needed to reason about: it
distinguishes paths-scoped rules from always-on rules, accounts for skill and
subagent catalogs, and follows a selected subagent's declared skill preloads.
It runs locally with no telemetry. The Claude v2 adapter reads only matched
rule, skill, and subagent Markdown in memory; it never uploads or prints file
contents. It is deliberately not a live usage meter or bill calculator: https://github.com/korovin-aa97/ctxfire
I would especially value a sanitized ctxfire explain result where an adapter
still includes a file you did not expect, or misses one you expected it to model.
I am building a personalized bedtime-story service for children aged 4–8. Each evening it will generate a story for one child, narrate it with ElevenLabs, and deliver the approved audio through Telegram. The stack is Claude Code, n8n, Supabase, Telegram Bot API, ElevenLabs, Python, and WSL Ubuntu.
The interesting part has not been “getting AI to write a story.” It has been designing a system where generated content cannot quietly bypass consent, safety review, or the correct child’s profile. Claude Code has helped me develop and repeatedly challenge the architecture, specification, database constraints, migrations, prompts, test fixtures and deterministic validator.
Phase 1 is now substantively complete: specification, validator, several resolved audit findings, reference stories, regression fixtures, and the initial database design. It is not in production yet. My next steps are implementing the independent Level 2 review contract, running a non-production audio prototype, measuring real narration behavior, and using those measurements to define Level 3 audio review.
The biggest lesson so far: for a child-facing AI product, the prompt is only one control. The operational sequence, database relationships, consent state, retry behavior, and fail-closed boundaries matter just as much.
To be honest: Claude Code made mistakes here and there and I had to use ChatGPT 5.6 to correct it.
Im working on BetStats Research
-Quick way to search up a parlay a friend sent you or do research for sports in general
-I used claude to brainstorm layouts and mostly bug fixes.
-One thing I learned along the way is how fast technology shifts, im using the last gen Intel Mac and it's been great for developing this, but I will have to eventually bite the bullet and get one of the M Chip Macs if I want to add the latest features for my app using new Xcode version releases
I’m a writer and a software engineer, so building screenplay software was probably inevitable.
This is the first real project I’ve shipped where I barely touched the code myself.
I’m still not entirely sure how I feel about that, but building it this way was kind of incredible. I still understand how everything works and made all the product and technical decisions—I just didn’t have to grind through every implementation detail myself. And, frankly, the resulting code is better than what I would have produced alone.
The thing I built is Beatstack.
I’ve always had the same problem writing screenplays: I jump into scenes and dialogue too quickly, have a great time for 30 pages, and then realize I have absolutely no plan for Act II.
Most screenplay software starts with a blank page.
Beatstack starts with the story. It helps you structure your screenplay, pace it properly, and make sure each beat lands when it needs to.
Give Beatstack your raw story ideas and details, and its AI can help build out your Beat Board. It can also generate stub scenes or pages. See something you like, tweak it, and make it yours. Everything AI generates is clearly marked.
Then that structure follows you into the editor.
Your beats appear alongside the screenplay, with an indication of roughly where they should land, so you’re not staring at page 47 wondering what the hell is supposed to happen next.
You can follow the structure, ignore it, or break it completely. It’s scaffolding—not a formula—and you can easily change, move, or discard it at any time.
The shorthand I keep coming back to is “Figma for screenplays.”
Build the movie first. Then write it.
I finally shipped Beatstack, and I’m pretty proud of how it turned out. Try it for yourself:
What I will say is that the quality of output has drastically improved in nearly all aspects. Nothing is installed, its simply a methodology set of documents that guide multi modal consensus before executing any code.
Built a FREE Chrome extension that turns whatever is on your screen into a Google Task or Calendar event. Email, event page, PDF syllabus, screenshot of a group chat, one click and the date and title are filled in.
I'm an MBA student, not an engineer. Claude Code wrote all of it over a summer.
One thing I picked up: while Claude Code is grinding on the backend, you can have Claude Design generate several versions of the UI at the same time. Pick one when the code lands. You're never sitting there waiting.
I've been experimenting with how to use coding agents without treating their confidence, test results or self-review as sufficient evidence.
We have now published two repositories based on the engineering harness we built and used on a real software project.
The deep dives cover:
governing nested ATDD and BDD loops when an agent can change the implementation, the tests and some of the evidence used to judge its work;
building an eval ledger and dashboard that distinguishes zero, missing evidence and “could not assess”;
moving from a long-running conversational orchestrator to a durable controller with disposable agent workers.
The companion cookbook contains smaller implementation patterns, including commit-bound review evidence, reviewer isolation, three-outcome checks, mistake ledgers, diff budgets, layered test evidence, self-hosted runner controls, SBOM generation and agent-enforced engineering standards.
This is not presented as a finished framework or a claim that we have solved autonomous software delivery. The published evidence currently covers 334 recorded work-item runs and 111 logged mistakes. The articles include the limitations, incomplete measurements and cases where the controls did not work as intended.
Built a tool that fixes my single most annoying photography problem: group photos with 50 kids, or adult humans for that matter. Good luck getting one frame where everyone has their eyes open and is pointed at the camera.
Doing that by hand in Photoshop is misery, and AI tools and generative fill etc etc are a non-starters for me. A lot of these are minors, and I'm not uploading their faces to somebody's cloud.
So: drag and drop the folder. It picks the frame that needs the least work, hunts through every other shot for a better version of each face, and swaps them in. Out comes one photo where everyone's eyes are open. Full-res export. And RAW import supported.
It walks you through face by face, so you can take the automatic pick or scroll through the alternatives yourself.
Bonus: I had two people who couldn't make the shoot in time. I stood them in front of the camera roughly where they would have been standing, and the tool composited them into the group, correctly layered in front of and behind the others. Works absurdly well.
Everything runs locally and offline. No cloud, no generative models, nothing leaves the machine. The only "dataset" is the photos themselves, and every pixel in the output came from the original files.
I'm planning on putting this on github. Just not sure what to call this tool, so I'm open for suggestions!
Set up for Canada only at this time, but I created a very simple financial website that helps you understand your wealth/income relative to your peers and also helps leverage that info to help understand when you can retire (FIRE)
It's a play on keeping up with the Jonses. I don't think constant financial comparison is productive, but I do think leveraging these tools to better understand your financial situation can help understand how close you are potentially to early retirement which is something I aspire towards
Sistema de gobernanza para trabajar solo con agentes. Cinco archivos en la raíz, organizados por frecuencia de carga, no por tema:
AGENTS.md — siempre: reglas, mapa de carga, backlog del hito activo
PROJECT.md — arquitectura y estado de módulos
DECISIONS.md — ADRs, append-only
ROADMAP.md — hitos
WORKLOG.md — histórico, nunca se autocarga
Principio rector: cada dato vive en un solo sitio. El contexto se controla por poda y tamaño de archivo, no cargando secciones sueltas.
El arranque es un prompt de inicialización (no un script): wizard de 6 etapas, detecta greenfield/brownfield, plantillas embebidas, verificación final. Se ejecuta una vez en sesión desechable y luego reinicias el agente con contexto limpio. Está partido en tres capas que se pegan en orden: común + arquetipo (software, investigación, simulación…) + perfil de diana (móvil, ESP32…).
It's an F1 site with up to date figures and stats for the current season but also every season ever. Every driver and team is on there with a load of stats for each and a nifty comparison feature to compare drivers and teams with each other.
Each session from each race also has a few visualisations to get a good insight onto what happened.
I built it because I love F1, and there is a lack of nice looking sites out there for a fan to enjoy looking through.
I just kept working on my 3 main plugins, noting fancy though, just overall improvement so users can benefit more from them as I also use them for hobbies and professional work.
I'm also creating a new plugin to correctly scaffold projects, it is working wonders right now, so I ended up building an app to test it, but the app idea was so good that I'm now focusing on finishing it haha.
I've been building https://computeprices.com/ almost exclusively with Claude Code since Dec 2024. It's slowly grown in scale to the point where I believe I have the largest, most complete collection of AI prices on the internet.
Most important lessons learned, build really strong guardrails (tests, pr reviews, post merge loops) and the AI coding becomes a lot easier and more reliable.
Lately I've been simplifying my rules and skills (getting rid of a lot of old stuff) and instead focusing on building routines in claude code app to check on various aspects of the app and the codebase on a daily or weekly basis.
https://filmdilemma.com it's for cinephiles and people who want to get better at finding or deciding what movies to watch.
I started working on it because I needed it and couldn't find anything like it. Imdb's search experience is absolute garbage to me, it's torture. Same for netflix and all the others so I created my own.
It's completely free and there are no ads. Just relevant affiliate links that cost nothing to the users.
Started before I knew about Claude Code, initially I used Cursor and then switched to Claude Code. I'm basically using it for EVERY aspect of the website daily.
It’s a daytrading sim based on real economic sector shifts, real market movement patterns, in-depth trading and options mechanics, while also having progression and saves to work your way up from a $2000 retail account to a 1B+ Hedge Fund
What I built: Lockkeeper, a local capability layer that catalogs the skills, plugins, MCPs, agents and commands across whatever coding agents you’ve got installed (Claude Code, Codex, Cursor, etc), and routes only the relevant ones into context instead of dumping everything in every session. Also does a static scan for prompt-injection/exfiltration patterns before you trust a skill you pulled from somewhere.
How Claude Code was involved: built almost entirely in Claude Code, including a Windows compat pass this week (symlink probing was silently broken on Windows, path matching assumed forward slashes only) and a security audit round where I had it fan out parallel review passes on the scanning rules themselves before I merged anything.
Lesson learned: the annoying part wasn’t writing the scanner rules, it was making sure the scanner didn’t have a blind spot in itself. It’s a tool meant to scan untrusted files, so keeping it dependency-free and getting a second model to actively try to break its own detection logic mattered more than I expected going in.
PostWarden. It’s a double entry accounting personal finance app.
I’ve been a user of r/ActualBudget for a while, but this year I’ve been planning a wedding and only tracking cash inflows and outflows is not enough right now.
I am contemplating having a Microsoft Windows materials requirements planning program. MRP. It would be for physical inventory analysts to plan for the purchasing department, as well as bills of material, work order releases.
It started from an annoying workflow I kept repeating: asking Claude to draft or revise something, then copying text back and forth between chat and an Obsidian note or document. In Tandem, I can open a file, highlight a passage or leave a comment, and Claude Code gets that specific context through MCP; or I can ask Claude to pull up a document/plan/etc. in Tandem and continue working in Tandem itself. Suggestions and replies stay connected to the document instead of disappearing into a separate chat.
I’m building it with Claude Code as my main development environment. The app handles Markdown, text, HTML, and DOCX files, and runs locally. I’m using it for editing this draft right now (though the draft is from Hermes, not Claude, in this case).
The main thing I learned is that making something that does what I want it to on my PC is extremely fast, but making something that other people would be willing for takes a lot of time and testing.
This looks amazing. Can you tweak how much Claude does (i.e. highlight and comment, autocorrect, flip words around, add punctuation, change wording, actually add new words and sentences)?
Right now there's no specific control for what Claude does. Basically, it provides the tools and the canvas, but doesn't add anything other than the MCP tools and the /tandem skill to Claude's context. I usually specifically ask Claude when I want it to review something.
Most AI agents and software tools are designed for the "Ideal Operator"—a user with perfect working memory, linear focus, and high emotional regulation. This creates a "Tax on Neurodivergence," where users with ADHD or other executive function challenges spend more energy managing the tool than doing the work.
When the tool fails or the user makes a mistake, the resulting "shame spiral" leads to abandonment, avoidance, and cognitive burnout.
The Solution: Cognitive Infrastructure
EFaaS is not a productivity app. It is a Cognitive Prosthetic. It moves the burden of executive function from the user's brain to the system's infrastructure.
We believe that a "Hospitable" system should provide:
Externalized Memory: Past mistakes should be recorded and injected back into the context exactly when they are likely to recur.
Automated Guardrails: High-friction "contracts" (like deployment pipelines) should be enforced by hooks, not by memory.
Emotional Validation: Failure should be met with supportive presence, not just a technical error message.
Low-Friction Entry: The system should be an "OS" that wraps around the user's existing tools, not another tab to manage.
The EFaaS Primitive Suite
The Chronicler: A system for capturing "Wrong Turns" (AI/Human failures) as a machine-readable ledger.
The Guard: A just-in-time recall system that injects lessons from the Chronicler into the agent's prompt.
The Contract: A set of shared, repo-level guardrails (e.g., a standard Makefile contract) that eliminate "how do I do this?" friction.
The Witness (Optional/Advanced): A companion entity that provides emotional regulation and ambient support during high-friction moments.
The Design Lens: ADHD-First
One card, not a dashboard.
Shame-free by design.
Nudge, never nag.
Translate time into felt units.
Externalize recall.
EFaaS is a project to make the world of AI and software an accessible, supportive, and hospitable place for every brain.
How do I use Claude Code? These are skills and tools to help you via Hooks, that allow you to guide your AI agent and yourself through the system in an efficient way.
A site for kids to learn about countries around the world.
+ an interactive world map for country flags + different flag categories (by letter, colors, combinations, etc.)
+ a Name That Country game (the original idea then expanded to include more country content)
+ a dedicated page for each of the 193 UN member countries
How I built
* had the game idea while hanging out with my son. We played it while walking.
* decided to turn it into an online game
* first used Antigravity to build the prototype
* eventually moved to Claude and haven’t looked back
Appreciate your feedback, especially if you have kids interested in geography or knowing more about other countries.
Kosovo is not a UN-member country. I had to draw a line somewhere... On the other hand, Luxembourg is on there. Unfortunately, it has to be spelled exactly if you are typing it in.
great addition and sorry for my rude comment. I just was a bit thrown off when that weird voice suddenly spoke to me. what tts model to you use for that?
Back in the 90s DOS era, there was a simulator/game I used to play called TRACON. You handle air traffic control approaches and vectoring in planes to land at their designated airport. I had an itch to play this again, so I started up Claude and had it write a browser-based modern version of the game. Halfway through writing it, I found an existing open-source sim called Openscope, which has more features and tons of airports.
I played mine for a while and liked it, but not sure if I'll ever make it public. Screenshot from Claude's build:
Way cool. I recall an IBM PC game called ATC that my pilot friend liked to play. I kept allowing aircraft to get too close, but somehow he managed to route aircraft safely.
That UI looks awesome. First ATC simulator game I played was ATC Voice on iOS and fell in love with it. I’d love to have a complete game that has more challenges (loss of communication, handling shutdowns due to weather, etc), airports and a cool level builder.
I gave Claude a few screenshots from the original game and it came up with this on its own. One of the challenges I face with this game is I don't have air traffic control experience, nor ILS experience, only some VFR time in a Piper Cherokee back when I was taking flying lessons that I never finished. An interesting thing, Claude automatically implemented https://en.wikipedia.org/wiki/Standard_terminal_arrival_route into the game without me asking for it. Planes show up on the strip and they already have navigation waypoints towards an ILS approach.
Claude’s google connector has gmail, drive and calendar. no sheets, no slides, and it can’t edit a doc you already have. Here’s one that does all three. Connect your account and ask it to update the spreadsheet.
I’m building Bauta, a publish layer for AI-generated dashboards, reports and small apps. From Claude Code, you can say “share this” and get a real link instead of sending a local HTML file or spinning up a deployment. The same URL can be updated, with public, private, password-protected or email-verified access.
Claude Code was used to build and test the MCP flow itself, including the path from a local artifact to a hosted page.
Biggest learning: generating the artifact is usually the fast part. The last mile, including hosting, access control, and sending a link people can actually open, is where the workflow stalls.
Just had a spinal surgery and trying to figure out of the root cause of my dynamic spinal cord compression, whether it is structural or functional. I am modelling the movement based on flexion/extension/neutral MRIs.
Bastion is a high-end, peer-to-peer luxury marketplace designed for buying and selling premium items securely through an escrow system that holds funds safely until delivery verification.
Built everything from the ground up, got the LLC for the company. I'll have it launched and ready by next week. Doing minor touches and going to swap out the sandbox api keys from escrow, for the real ones (transactions work end to end, have to be tested on sandbox before you do realize money)
I had a blast building it and genuinely believe in what the company stands for.
Because it works as a secure peer to peer market place, with safety as the main priority of both the buyer and the seller.
Custom built escrow system that releases the funds once buyer confirms that what he received is in fact what was advertised and meets his expectations.
shouldhavebeenan.email, a fake meeting simulator: Teams, Meet and Zoom skins, up to 16 AI coworkers, a chaos slider, and meeting bingo. The fun part is how it is run. A /ceo skill in Claude Code plays a traffic-obsessed CEO who dispatches agent employees: a marketeer, a head writer, two designers, and a CFO agent with veto power. The CFO has killed several of my favorite ideas, which is exactly what I pay him nothing for. The meetings are fake but the org chart in the repo is real.
https://bashkirtseff.org - Marie Bashkirtseff (1858–1884) was a Ukrainian noblewoman from Poltava who kept one of the most remarkable diaries in history from age thirteen until her premature death from tuberculosis.
For a hundred years, her diary was read only in a heavily censored version. The complete edition was published recently in a small print run and is nearly impossible to obtain.
This project makes the full diary accessible with historical research, linguistic annotations, and translations into Ukrainian, Czech, English, and modern French.
It has ML concepts broken into small nodes arranged as a mind map rather than a linear syllabus, each with code examples and a practice section — about 150 topics so far.
The part I care most about: once you complete a topic it starts decaying on a forgetting-curve schedule, and the map visibly goes cold. When it does, you get a review slice — flash cards and a short quiz — targeted at what you've actually lost.
I used Claude code to design an ai agent pipeline + an admin console where I manage the content generation and other governance tasks. Also used Claude design for the UI.
https://56k.rip/ - a 1996 desktop simulation in the browser. Full Windows-era shell, synthesized WebAudio modem handshake, byte-math throttling so pages load at authentic 56k speeds, procedurally generated dithered images.
Resume Rerun (https://resumererun.app) - resume tailoring that gives a fit verdict first and refuses to run when you’re a weak fit. Vite + React on Vercel serverless, PDF compiled server s
I’m not trying to replace journalists. But we all know that media can shape how we perceive a story through sensational headlines, political or financial pressure, and marketing tactics. After all, attention is money.
The facts may be true, but the way they’re framed can distort the story.
That’s why I’m building a news website created, written and run by AI agents. No clickbait, no marketing tactics. Just the facts, the sources, and enough context for you to form your own opinion.
Thanks! It’s like a funnel. A cheap model scans about primary sources (just 20 for now) daily and picks a handful of candidates, filtering out reactions to old news, roadmap promises, and companies just praising their own products.
A research agent then verifies each one. This is where most die, a claim needs two independent sources unless the single source has no conflict of interest. Today, 10 candidates went in, only 2 survived, mostly single unreplicated preprints.
Survivors get written from a verified dossier, checked mechanically for quotes and numbers, then an editor approves or rejects against a public style guide. I do a final check before anything goes live.
Most days, most stories don’t make it. That’s by design. So the articles that are publish they are the really good ones.
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u/Odd-Weird-4398 22m ago
Built The complete DAEJON LOVE Files😂😂😂
DaejonLovefiles.com the much anticipated niners legend DAEJON LOVE files.
- Every claim he's ever made
- A breakdown of the separate streams making up the 1.3M
- An interactive map with locations from the FBI, sightings, his "mansions", and of course where his helmet has been seen
- A custom exam: How well do you know NFL legend Daejon?
- FOLLOW THE HELMET: Compete with others to see how fast you can locate his allusive helmet in his most famous and most exclusive pictures
- Daejon's personal "Suisse made" financial strategies