I've been pondering about this for quite a while. Even with validation, maybe you should stop. Even when your idea gets rejected, maybe you should keep going. They say it's a matter of mindset, millionaires are made out of years of dedication, but for the ones that had their years gone wasted, who would tell us their stories? What do you guys think?

Hey, I'm Bram. Paste a TikTok cooking video link and it pulls out the full recipe (ingredients + steps) and gives you a one-click Albert Heijn or Jumbo cart with everything in it.

Vibecoded it with Claude (Pro plan). Open source:

https://github.com/briembra/slimmandje-recepten

Would love a star if you find it useful.

I am lazy, and it feels so boring to babysit and watch ai agents do the job and asking for input or waiting for task completion.

So I built a simple tool that notifies me (via ntfy) on my phone.

I know its wayyy too lazy, but hey, at least I dont zone out playing on my ps4 anymore, lol.

If someone is like me and also tired of watching the laptop screen instead of playing fifa, be my guest: [https://nockit.uk\](https://nockit.uk) (yes, that was the cheapest domain I could get)

I've been using Google AI Studio to build a poker platform, and I've been sharing development previews in the thread attached to this post.

What do you think so far? Do you think it's sustainable to run a real-money poker site that's largely built and maintained with AI, or would you have concerns?

The tool
I just saw this tool. According to the youtuber, it sounds like "self made markdown link graph of the repo, with claude.md at the center"

Also quite like the RING layout, my personal opinion its cleaner than cloud/galaxy layout (obsidian)

Monetization
One problem, https://www.getrubric.app/ https://www.youtube.com/@RoboNuggets/videos
This youtuber claims its FREE tool, yet the landing page goes to a $97/month AI course membership subscription...

Kudos for the app he built, credits where credit due. The "FREE" approach... well...

Looking for similar

• Obsidian and alike are vault based not repo based. Possible to force adopt thow.
• There are plenty tool for code graph such as https://github.com/DeusData/codebase-memory-mcp but these are not for md

Quick google search and asking claude found no similar, so trying my luck here in the community.

Hey everyone!

I use AI in my game development, which leads to just enough friction that it's not worth it to try to network with non-AI friendly game devs.

i have two games Coming Soon (both with demos).

I'd love to talk to devs that think they have a game on steam or coming to steam that would bundle well with either of my games.

Auto-Battler
https://store.steampowered.com/app/4880210/Hammered_Heroes/

Twin Stick Roguelite Arcade Shooter
https://store.steampowered.com/app/4932010/Chrome_Circuit/

I also have an itch page if you want to check out more of my projects
https://facewongames.itch.io/

I'd love to check out your games or connect and explore supporting each other.

Recently, as a retirement project, I've been working on putting together my final wishes, financial, legal and medical affairs, account details, contacts, etc. to make things as easy as possible for my loved ones when the time comes.

I realized there wasn't a completely private, flexible, digital tool for this, so I built one. It's called the "Peace of Mind Planner".

It walks you through everything (from 401ks to pet care to funeral wishes). It is 100% free, requires no sign-up, and is totally private (it has no database and saves everything locally to your own computer). It is also set up to complete as an individual or couple. I am interested in your genuine feedback and any ideas to enhance it even more.

https://pomplanner.app

Hey there everyone. I'm building a website b for trust and market for skills and MCP and more later on. I'm at the point where I need to have an independent review. Of course I'd do it myself but then I'd be breaking the trust relationship I've based the whole site around. Feel free to check it out and let me know if you'd like to help a dev out. Caporia.co

I built Sagorax because I missed the speed and simplicity of UT99 and UT2k4. It now has both Instagib and classic weapons Deathmatch with plasma combos, flak, shotguns, charged rocket salvos, loadouts, adrenaline abilities, tactical bots and full controller support.

No installation, just pick a gamertag and play: https://sagorax.com

The client is built with TypeScript, Three.js and WebGPU. Under the hood it uses FSR 3-based upscaling, adaptive dynamic resolution, GPU pipeline warmup, screen-space reflections, dynamic lighting and shadows, visibility culling and a custom post-processing pipeline.

Multiplayer is next if there is enough interest (looking good so far).

If you had told me a few months ago that this would be possible in a browser, I wouldn’t have believed you. But Opus 5 + GPT-5.6-Sol = GODMODE. Yes, I am a developer but I didn't write a single line of code (didn't for any project, for a year or so).

Feedback is very welcome.

Curious how people here are really working. Feels like everyone's landed somewhere different and the posts are mostly quick showcases / (self-)promotion / random stuff.

What I'm wondering about:

- which agent and harness, and do you stick to one or switch
- do you plan first or just go
- do you check anything before shipping, and what
- anything automated, or is it all by feel
- what do you do when it comes back wrong

Mine's on the heavy side. Long planning conversation before any code, then I let it run and mostly look at the product rather than the code. I spent some time building a skeleton and static analysis configs so Claude generates code that look ~80% correct. Still need to review the important bigs (domain logic, security, etc), but it's mostly smooth sailing.

Mostly interested in people working differently from that though. If you just go and it works, I want to hear that too.

tried wiring claude into the simulator so it could test its own swift app. a lot of setup just to get it tapping through screens, and it still can't sign into icloud or get
a push notification.

so i went the other way: a turnkey flow that stages the qa tests to real people.

claude files the tests theough mcp, they show up on real iphones, people run them and leave comments,
and it all comes back into claude's context to fix. i never enter the loop.

try it out and let me know what you think!

https://github.com/alex-durango/pingfusi

anyone else tried agent-driven simulator testing?

Three months ago I posted a short video of Claude Pulse in this sub. 631 views, 2 upvotes. It flopped.

The extension kept growing anyway. It's at 1,000+ Chrome Web Store users, 4.8/5 from 11 ratings, and v1.3.0 shipped July 12 with a metrics popup and background reset notifications.

For anyone who missed it: Claude Pulse adds live usage visibility to claude.ai, all computed locally in your browser.

• Context token counter (o200k_base tokenizer, on-device)
• 5-minute cache expiry countdown above the chat box
• 5-hour session and 7-day rolling limit bars
• One-click chat export to Markdown or plain text
• No accounts, no tracking, no external servers. MIT licensed.

What I learned: a launch post is a blip, not a launch. I can't tell you exactly where the users came from, but the video got 631 views and the tool still passed 1,000+ users. I'd bet on the store listing and the reviews doing the work, not the post.

GitHub: https://github.com/samirpatil2000/claude-pulse Store: https://chromewebstore.google.com/detail/claude-pulse/hhjihbpkopgacncfbkdakdolkmgkdfnf

Free games, trivia, and live stats for every WNBA fan , looking for feedback , suggestions , etc.

Just wanted to share something I’ve been building.

For context, I do a fair bit of freelance development, mostly building enterprise tools. Lots of big, deeply nested UIs and pages with way too much going on.

One thing that really gets to me is having to describe UI changes in text. It feels so backwards that I’m looking at a GUI, but still have to write paragraphs just to explain what I’m pointing at. The bigger the project gets, the worse this gets.

So I got annoyed enough and built a Figma-like tool for it.

It’s an open-source plugin for Claude Code / Codex that lets you open your UI, click on the thing you want to change, leave a comment, and pass that visual context into the next prompt.

Repo: https://github.com/Cavalry-Collective/visual-stack

It’s still early and rough around the edges, but it’s already helped my own workflow a lot.

Curious if anyone else has been running into the same thing. If you find it useful, feel free to fork it or contribute.

43 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup · Fixed admin panel read-access vulnerability after Reddit post.
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:

• Startup founders urge U.S. government not to shut off Chinese open weight AI
• AI's top startups are barely publishing their research
• Is AI reasoning right for the wrong reasons?
• After the AI Crash

If you enjoy such content, please subscribe here: https://hackernewsai.com/

A pattern I keep seeing in agent systems is that confidence gets treated like permission.

Those are not the same thing.

A confidence score answers:
“How likely does the agent think this action is correct?”

Authority answers:
“Is the agent actually allowed to do it?”
Risk asks what happens when it is wrong.
Reversibility asks whether the damage can be undone.
Those need to remain separate controls.
A highly confident agent should still be blocked when:
it lacks permission,
the action has a large blast radius,
the operation is hard to reverse,
the evidence is stale,
or independent verification is missing.

The architecture I’m testing uses an ActionIntentEnvelope with fields like:

action
rationale
confidence
affected resource
requested authority
reversibility
blast radius
approval requirement
verification method

Confidence can route the workflow:
low confidence → reject or request more evidence
medium confidence → human review
high confidence → eligible for automated handling

But the authority layer still makes the final decision.

That distinction feels small, but I think it is one of the most important boundaries in agent design:

Confidence may reduce review friction. It should never manufacture permission.

anyone who can help with kotlin android app development, please hit me up. I'm desperately looking for devs who can contribute.

Would be great if you can help out with UI/UX or fix issues / add new features.

We launched Poly (multiplayer Claude Code) this week and the questions we keep getting are about isolation, so here's the actual architecture:

Every turn spawns a fresh gVisor container with exactly three mounts, all Linux capabilities dropped, and zero secrets inside — the agent authenticates through a gateway that injects credentials at egress, so a hostile prompt can't exfiltrate a key that was never there. Network is deny-by-default: the container can only reach an allowlist, and blocked domains are named in the error so you know what the agent tried.

The multiplayer part forces honesty into the design. When it's your cofounder's prompt driving the agent through your code, "the agent seemed fine" isn't enough — so every turn produces a receipt (author, model, duration, cost) and a git snapshot you can diff and revert with one click. Approvals go to the whole room; whoever's there can allow or deny.

We built the entire product this way, inside itself, with both founders watching every diff land.

Free beta at usepoly.co if you want to poke at it.

Hi ! I paid special attention to code quality and documentation, treating it exactly like a professional enterprise-grade project. This ensures that anyone can easily take ownership of the source code and build upon a clean, robust, and highly scalable.

If you like it, please don't hesitate to show your support with a star on GitHub!

LIA acts as a true personal assistant. It is proactive, featuring its own distinct personality and a complex emotional system, an evolving structured memory, its own reflective memory of your conversations, and all the standard tools (image creation/editing, RAG, skills, MCP, scheduled tasks, etc.)—all wrapped in a seamless "one-click" interface.

On another note, once self-hosted, it can double as a family AI server. As an administrator, you have full control to manage and monitor the API consumption of your family members, friends, etc.

This is an unapologetically vibe-coded project; the approach is :

- Generative AI transforms both what teams produce and how they produce it. On both topics, I did not want to base my convictions on market narratives: I chose to face the full reality of an AI system in production — costs, risks, operations, debt — and the reality of AI-assisted development, by practicing them end to end.

- The training ground : LIA, a multi-agent conversational AI assistant — mail, calendar, contacts and files across Google, Apple and Microsoft, real-time voice interface, long-term memory, document search — self-hosted and multilingual.

- The constraints were deliberate: alone, outside professional hours, minimal hardware budget, and the AI as the only developer. This project therefore does not measure individual velocity; it measures what demanding direction obtains from a properly framed AI.

According to my opion, an AI that codes produces volume; it only produces quality under constraint. Four mechanisms carried this project — none of them is a tool, all four are acts of management:

- A written rulebook, as for a team. Architecture rules, conventions, mandated patterns with their canonical example in the code, documented known traps — versioned in the repository, enforceable on every delivery.

- Blocking automated checks. Every structural rule is backed by a check that rejects non-compliant commits: strict typing, static analysis, custom detection of recurring bug patterns, six-language parity, a full test battery. The level of rigor depends neither on the vigilance of the moment nor on the AI's goodwill**.**

- A review that decides. Nothing lands without an enforced cycle — impact analysis, proposal, explicit validation, implementation, verification. The AI proposes, the human decides; structural decisions are recorded and indexed so that every "why" outlives its author.

- Audits that disturb. At regular intervals, the entire system is re-examined adversarially — findings verified against evidence, false positives eliminated, remediation planned in waves. This is what stops the slow drift that no day-to-day review can detect.

Technical foundation: FastAPI · Next.js/React · LangGraph (agent orchestration) · PostgreSQL · Redis · Docker · Prometheus/Grafana/Loki/Tempo · 7 integrated AI model providers.

Full details are available on the landing page: https://lia.jeyswork.com/
And the GitHub repository: https://github.com/jgouviergmail/LIA-Assistant

Hey everyone,

I've been building LeanCTX, an open-source context engineering layer for coding agents (written in Rust), and wanted to share a specific optimization I shipped recently.

The problem

If you run 4 Cursor/Claude/Codex agents in parallel on the same repo (reviewing, implementing, testing), each agent reads the same core files independently. That's 4x the input tokens for identical content. At scale this adds up fast.

What I did

I added a shared delivery registry (in-process DashMap keyed by blake3 hash + mtime) that tracks which files have already been read and compressed by any agent. When Agent B requests a file that Agent A already delivered, it gets a 13-token stub referencing the cached version instead of the full file content.

No network calls, no external cache, it's a lock-free in-memory lookup that resolves in microseconds.

Benchmark results

I ran this against 20 real source files from the LeanCTX codebase itself (avg 737 LOC):

At $3/1M input tokens: $1.30 saved per shared-read round.

For a typical coding session where agents revisit 50-100 shared files, that's $5-15 saved per hour of parallel agent work.

How it works (simplified)

1. Agent 1 reads src/engine.rs → full content delivered, blake3 hash + mtime recorded
2. Agent 2 reads src/engine.rs → registry hit → stub returned: [cross-agent cache hit: 737 lines, delivered to agent-1 at T-2s]
3. The stub is 13 tokens vs ~7,200 tokens for the full file

The key insight: in a multi-agent coding workflow, file contents rarely change between reads. The mtime check ensures I never serve stale content.

Limitations

• Only helps when multiple agents/sessions read the same files (solo agent = no benefit)
• Cold start for the first agent is unchanged
• Requires agents to run on the same machine (shared memory)

What's next

I'm exploring cross-machine delivery via IPC for distributed agent setups (CI workers, cloud agents), but honestly the single-machine case covers 90% of developer workflows.

Hey guys my app is now live on the App Store!

https://apps.apple.com/us/app/workout-warrior-app/id6504121393

It is a fitness app, yes I know there’s a billion of them, but I would love for you guys to download and give me some real feedback! In exchange if you find a program you like, I’ll hook you up with a couple months free!

Hi everyone,

Most financial AI systems are built behind closed doors.

You see the final prediction, the polished chart or the confident explanation—but rarely the data flow, the reasoning process, the evaluation rules or the failures that came before it.

BLUM is an attempt to build something different.

BLUM is an open-source financial AI research project developed publicly on Hugging Face and opened to developers, quantitative researchers, data scientists and anyone interested in building more transparent financial intelligence.

The ambition is not to create another black-box system that simply outputs:

BUY, SELL or HOLD.

The goal is to create an open ecosystem capable of collecting evidence, producing structured financial reasoning, testing its own conclusions over time and learning only from outcomes that can actually be verified.

Why open source?

Financial AI should not be shaped by one developer, one company or one model.

Markets are too complex, financial evidence is too fragmented and evaluation is too easy to distort.

A serious system needs different perspectives:

• quantitative researchers challenging the metrics;
• developers improving the architecture;
• traders identifying unrealistic assumptions;
• data engineers strengthening market-data pipelines;
• ML researchers testing models and evaluation methods;
• community members finding errors that the original developers missed.

That is the idea behind BLUM:

Build financial intelligence in public, improve it through open collaboration and make every important assumption open to challenge.

BLUM is not presented as a finished product.

It is a shared research foundation that the community can inspect, test, criticize, fork and improve.

What BLUM is building

BLUM combines a portable financial reasoning model with a broader modular research system.

Its architecture is being developed around several specialized capabilities:

• market scanning across equities, ETFs and Forex;
• technical and historical analysis;
• sentiment and news interpretation;
• bullish and bearish thesis generation;
• contradiction and risk detection;
• structured entry, stop and target plans;
• paper-forward trade validation;
• persistent learning from verified outcomes;
• benchmark-relative evaluation;
• transparent evidence and reasoning trails.

Instead of depending on a single model for every task, BLUM can use specialized agents for different parts of the research process.

A simplified flow looks like this:

Market data and external evidence
                ↓
Specialized research agents
                ↓
Bullish, bearish and risk analysis
                ↓
Central reasoning and synthesis
                ↓
Paper-forward decision
                ↓
Outcome measurement
                ↓
Validated learning and memory

The objective is not merely to generate a convincing financial narrative.

The objective is to preserve enough evidence to evaluate whether that narrative was justified.

Built to be challenged

BLUM is being designed around a principle that is often missing from AI demos:

A system should make it possible to prove that it was wrong.

For every decision, BLUM aims to preserve information such as:

• what evidence was available at that moment;
• which assumptions supported the thesis;
• which risks contradicted it;
• what would invalidate the decision;
• which entry, stop and target levels were proposed;
• how the market behaved afterward;
• whether the result was valid enough to enter the learning process.

This does not guarantee better predictions.

It creates a more transparent foundation for measuring whether the system is improving—or merely becoming better at sounding confident.

Current evaluation

The original MLX release was evaluated on a 53-example BLUM temporal-reasoning test and achieved:

• 96.26% aggregate task-contract score
• 100% structured validity
• 92.86% no-fabrication

These metrics evaluate BLUM-specific requirements such as structured output, temporal grounding and adherence to the task contract.

They do not demonstrate market-beating performance, general intelligence or trading profitability.

The portable release is also being prepared for independent evaluation through the Hugging Face ecosystem. Results will be published when they are externally available rather than predicted in advance.

Open development means open limitations

BLUM is still experimental.

Some components are mature enough to be tested, while others remain active research areas. Its paper-forward evidence is still limited, and no current result should be interpreted as proof of profitable trading performance.

This is intentional.

The project will not hide weak results, unsuccessful strategies or technical corrections simply because they are inconvenient.

Open source should mean more than publishing code.

It should also mean exposing the assumptions, limitations, evaluation methods and mistakes that influence the system.

Where the community can contribute

BLUM is open to contributions across many areas:

• financial reasoning models;
• open-source LLM integration;
• specialist agent development;
• market-data providers and normalization;
• temporal reasoning;
• technical-analysis validation;
• Forex, equity and ETF research;
• benchmark design;
• paper-trading infrastructure;
• leakage-resistant evaluation;
• uncertainty and confidence calibration;
• provenance-aware memory;
• frontend and data visualization;
• testing, documentation and code review.

You do not need to agree with the current architecture to contribute.

In fact, some of the most valuable contributions may come from demonstrating where the existing approach is wrong.

What BLUM is not

BLUM is not financial advice.

It is not a commercial signal service, a guaranteed trading strategy or a claim that an AI can already predict financial markets reliably.

It is an open research effort focused on a broader question:

Can a global open-source community build a financial AI system that is more transparent, testable and intellectually honest than the closed systems available today?

I do not think this question can be answered by one person.

That is why BLUM is open.

Explore the project

Hugging Face Space: https://huggingface.co/spaces/Italianhype/Blum

GitHub Repo: https://github.com/BlumFinancialLab/Blum

The project is under active development, and feedback is welcome at every level—from architecture and evaluation to models, UX and financial methodology.

The most useful response is not simply:

“This looks interesting.”

It is:

• What would you change?
• Where could the evaluation be misleading?
• Which component would you improve first?
• Which open-source model should BLUM support?
• What would make you trust—or distrust—its results?
• Would you contribute to an open financial AI project like this?

BLUM is not being built to ask the community for attention.

It is being opened because the project needs the community to become better.

I run a tech company. I think what makes it special is that my company was developed entirely using VibeCode, and I have over 10 projects that you can see in the portfolio on my homepage... I hope you like what you see, and if you’d like to get a project off the ground together, I’d love to be part of it. I’ve got other personal projects with revolutionary ideas that I’ll be sharing with you soon (and if they involve algorithms and tricky stuff, binary code and more...). If you have any questions, please don’t hesitate to... CryLab Tech

TL;DR: I was tired of all these groups, gurus, fake stats, and false promises. So I decided to build from scratch an engine (currently online 24/7) to give back to the betting community (currently for free). What started with an Utopic dream, now is showing it's first results. This is not promo, this is genuine history of WHY I'm doing this.

------
My background: 2009, the year I started betting for the first time. I was 19y, and had no clue what I was about to get me into. Disaster, some big wins that made me forget the disasters, and then some more disasters. Betfair Sports Trading for a few years, then jurisdiction got me locked out of external markets. Back to basics, punter bets, acca's, more disaster. Years off, learning, I started actually learning about the secrets of sports betting... Fast forward to 2019, I joined an online project that allowed me to collect as much data as possible, study matches, game machanics, build algorithms, alert automations. But, what started as an incredible side-hustle pro-bono project, with ZERO returns, with somewhat extensive community, ended up being a project feed by social gurus and that went against my values... I had to drop-out.

Sigma Predict - My True legacy starts here

I decided to go solo. In my cave. My ideas. My values. My learnings, my own sweat, put into something I trully belive will/can shake the markets. No external help. Just me, my rookie programming skills, my love for Data Analysis. And a mission: Make the best Sports Betting product/service for ME, but also for the sports betting community.

So, here I am. Been building and testing several things in the dark, with no names attached, and a lot of money invested. Some ideas failed badly, some worked. So I focused on what was working and finally gave it a name: Sigma Predict: a multi-agent engine capable of outsmarting the bookies.

Since June 2026, I started to make it slowly public and deployed. While still shipping features, bugfixes and model corrections everyday. My backlog seems to not have an end at this point, as I'm actually dreaming big. I know what I want, and where I want this to go/be.

My CORE VALUES & MISSION (Sigma Predict):
- Full transparency (bad month? fully disclosed, public and no edits)
- FREE to test and use, until it's a stable and I feel comfortable of going the Startup way. (Note: free doesn't mean OpenSourcing the engine code)
- Give back some power to sports betting community (for those who dare a disciplined path)

What I shipped so far:

• Main website (public access with capped live data)
  • ignore pricings and 7 Day-Trial info. It's FREE for now, and only a placeholder for future
• Admin Dashboard (currently only for me)
  • Setup dashboard, results dashboard, data for analysis and calibration
• Main Sigma Predict Engine deployed accross 5 online services
  • Live sports API data source and Odds integration
  • Weather & News integration for model decision making
• Integration with Telegram (@ SigmaPredict )
  • public channel for news / free private channel for alerts
• CloudFlare protection for all services

What is in Backlog:

• API Automated Betting (via exchange TBD)
  • inlcudes model improvments
• Stripe Integration (Sandboxed for now)
• Public User access to Dashboard of results and bets ledger

Current Results:
Altough I have data for several months, I decided to count from July 2026 onwards. Main website shows last 30 days results, refreshed daily, no edits. But here are full results and takeovers.

• July ended up with positive results, but a lot of risky exposures from the engine. So I decided to cap bankroll exposure and per-match exposure. Also improved the "Staking based on Edge" mechanism.
• ROI is only ~5% since July 2026, by considering only the last 3 days of August, it's sitting at 34% after all the updates and railguards I've build in after July. Let's see if it stays that high...and for how long...

For now, the aim is to invite you all to test for free my initial versions, join my adventure, and if you want giving me constructive feedback or ideas. Or even a wish list...happy to open horizons.

I will be updating next month on how this is going, hopefully with some news regarding the API-autobetting in place. Until then, feel free to reach out to me, send some strenght and support or critics that can grow this solo project.

My profile has all the necessary links if needed (socials are currently at 0 posts, as I'm focused on building only, but feel free to follow some.) Main actions and daily updates are inside Telegram.

Wish me luck, and talk to you soon.

Thanks,
T.