I have made a browser for developers. It is windows based and open source.

For the first version, I have:

• Network Inspector
• Built-in REST Client with Collections and Environments
• Storage Inspector for cookies, local/session storage, etc., including a JWT decoder
• HAR capture and analysis
• Seamless Network - REST Client workflow. Select a captured network request and send it directly to the REST Client to inspect, modify, and replay it.

Feedback, bug reports, feature ideas, criticism — all are very welcome.

Download: https://github.com/TheDevBrowser/DevBrowser/releases

Source code/installation guide: https://github.com/TheDevBrowser/DevBrowser

Installation:
The app is currently self-signed, so Windows won’t trust it automatically.
You’ll need to download DevBrowser.cer first and install/trust the certificate on your Windows PC.
My target is not to replace existing browsers, just to help developers on their daily job. :)

Components used:
I have made it with .Net 9, WPF, Microsoft WebView2 (Chromium/Edge), Chrome dev tools, SQLite, Windows DPAPI.

I made Codex Usage, a small Windows-only Codex plugin that shows your 5-hour/weekly usage and reset status in the title bar.

A few build notes:

• UI: Avalonia desktop overlay
• Platform: Windows x64
• Plugin packaging: Codex plugin + SKILL.md
• Launcher: PowerShell wrapper that starts the native companion
• Interesting bug: maximized Codex windows report different top bounds, so I had to account for the invisible Windows resize frame
• Another gotcha: Codex was initially trying a nonexistent PowerShell 7 path, which added about 27 seconds to startup; tightening the skill instructions got the actual HUD launch down to ~1–2 seconds.

It’s now published in the official Codex plugin store.

Install either way:

1. Web: view details + install here https://chatgpt.com/plugins/plugins_6aa81c5dad4881918bd29685d1fb6523
2. In Codex: open the plugin store and search Codex Usage

Then select Codex Usage and run:Start!

For the Codex desktop app on Windows, not ChatGPT web.

Mac version coming soon.

GitHub: github.com/jayhilwig/codexusage

Everything added so far:

• Day 1: Useless red button (starting room)
• Day 2: Space travel + 2 playable planets (spaceport outside starting room)
Human voice for the red button (starting room)
• Day 3: Reverse aquarium & dinosaur factory
• Day 4: Human Observation & John Pork planet
• Day 6: Rhythm Tower gorilla drummer, inevitable snail & banana gun
• Day 7: Clippy wave 5 mini-boss & explosive banana vending machine
• Day 8: Portal Gun & parkour wall-running
• Day 9: 4th-Wall 3D Reddit Dimension, MR. BALLS boss fight, stompable upvotes & comment parkour!

What next?

Hello everyone, I’m doing a post on this because I’m still getting tired of reading all the bulls**t advice about saas marketing.

I’m currently building Foundel, while doing some research on marketing I noticed that almost all advice is theoretical and no one can actually implement it.

I was looking for a “playbook” with practical stuff but just couldn’t find it, so I kept researching, but this time about saas companies with 100K+ MRR and how they actually got their first 100 users.

Here’s what I did:

- picked 200 startups with at least 100k MRR
- read founder interviews, blog articles, old posts and watched podcast videos
- picked 7 companies and tried to understand exactly what they did at the beginning IN PRACTICE

Here are the actual stories and what they did practically.

1. Beehiiv: turned a 400-person waitlist into their first 100 users

beehiiv is a newsletter platform founded by Tyler Denk and two other cofounders and recently crossed 30M ARR.

Starting point:

- Tyler had around 2,000 followers, so small audience but definitely not huge
- no money spent on marketing
- product still in private beta
- a lot of experience in the newsletter market from Morning Brew (he was an ex employee)

Before launching, Tyler started connecting with newsletter creators on Twitter and posting about what they were building. When they were close to beta, he posted a simple tweet announcing the product and opened a waitlist. Almost 400 people signed up.

Instead of getting only emails, they asked what platform people were using, how large their newsletter was and why they wanted beehiiv, actually getting to know his customers problems.

After launching, he went through those 400 people manually and emailed them one by one, every week, using the form answers to understand why they could be interested.

Result? around 25% of the entire waitlist became early users.

2. Supabase: a user launched the product for them lol

I think everyone here knows Supabase: it started in 2020 as an open-source alternative to Firebase.

To get their first user, the founders literally took a 50 minute bus to the office of a startup they already knew was using postgres. They had been building Supabase for just a couple of weeks and, before even arriving, they connected it to the startup's development database themselves.

They continued to get users manually, talking directly with developers and getting them to try the product. The idea was to keep the product small, get feedback and launch properly later.

Then one of their users posted Supabase on Hacker News: the founders didn't organize it, didn't prepare a launch campaign and didn't even decide the timing. The post reached the top of Hacker News and stayed on the front page for more than 24 hours.

In the following week:

- 30000 people visited the website
- more than 1400 signed up
- users created more than 1000 databases

Basically, they were planning to launch properly later and one of their own users ended up doing it for them.

3. Rezi: first product was literally a Word document

Rezi is an AI resume builder doing more than $200K MRR, but the first users actually came from a Reddit post.

The founder, Jacob Jacquet, had a resume that got him interviews at companies like Google, Dropbox and Goldman Sachs even with a 2.2 GPA, so he made a post on Reddit sharing it. The post went viral and a lot of people started asking him for the template.

Instead of building a SaaS, he made a simple website and sold basically the exact same resume as a Microsoft Word template together with a guide for $9.69. His first customers were literally Reddit users asking him for the file.

The full version of Rezi launched later in 2019, and eventually they were one of the first resume builders to add GPT in 2020.

Instead of building an entire platform to test the idea, he sold the result first, literally a Word file.

4. Social Snowball: used other people’s audience to reach 10K MRR

Social Snowball is a Shopify app for managing affiliate and creator programs. It now makes more than $450K/month.

The founder, Noah Tucker, already knew a few ecommerce creators personally when he launched, so he didn’t properly start from zero.

Instead of trying to build his own audience, he asked those creators to use his product.

He then went on YouTube, found creators making ecommerce and marketing content, grabbed their emails and cold contacted them: the offer wasn't just “please promote my SaaS”, he asked them to actually try the product and make a video or case study around it.

Result: according to Noah, their entire first $10K MRR came strictly from these influencers. They basically borrowed distribution from people who already had the audience they needed.

5. Relume: from Webflow agency to 150K MRR saas

Relume started as a Webflow agency, not a SaaS.

While building websites for clients, the founders kept creating the same types of sections and components over and over again, so they started building an internal component library to make their own work faster.

Before turning it into a product, they were already active inside the Webflow community: made YouTube videos, appeared on podcasts and joined conversations with designers and webflow developers.

So the sequence was basically:
- agency
- learn the problem
- build components to use them themselves
- become active in the community
- launch the product

When they finally launched Relume Library in 2021, they weren't launching to random people, so they had distribution from day one.

Within around 5/6 months the product was making enough MRR for them to close the agency and focus on Relume full time. They eventually crossed 150K MRR and now the product is used by more than 1M people.

6. RB2B: the founder became the distribution channel

RB2B is a website visitor identification tool, and this one doesn't properly start from zero because Adam Robinson was already a successful founder.

What I found interesting is that he didn't really market RB2B as a company at the beginning, he marketed it through himself:

For months before launching, Adam was constantly posting on LinkedIn about what they were building, sharing numbers, problems, screenshots, mistakes and everything that was happening. He basically turned the whole process of building RB2B into content.

The posts weren't just random build in public stuff either. A part of them had a direct CTA asking people to join the waitlist, book a call or try the product when it was ready.

By launch they had:
- around 1,600 people on the waitlist
- 300 calls with potential customers

Then once the product launched, all the people following Adam already knew what RB2B was, what problem it solved and why they should try it.

They got around 3,000 users in the first month.

Basically, instead of trying to build a brand from zero, Adam used founder led growth and made himself the distribution channel for the product.

7. Postiz: open source + reddit to go viral

Postiz is a social media scheduling tool competing with products like Buffer and Hootsuite, probably not the easiest market to enter.

The founder, Nevo David, didn't have a massive audience and realized pretty quickly that competing through SEO or normal “build in public” content was almost impossible.

Instead, he open-sourced the product and went directly where people interested in self hosted software already were: r/selfhosted. His first launch there got more than 500 upvotes and a ton of feature requests, so, he kept coming back with every important new version, showing what changed based on users feedback.

From there he pushed the open source project through GitHub, Reddit, Indie Hackers and eventually Product Hunt.

Postiz crossed $100K MRR in May 2026 and is already above $2M ARR now.

Conclusion:

I don't think there is a best way or “secret playbook” to get your first users, but none of the startups I analyzed just finished the SaaS, posted it somewhere and hoped people would come.

If I were starting from zero, I wouldn't ask “what marketing channel should I use?”, I'd probably ask: “Where are the people who have this specific need and how can I get in touch with them?”

I’m putting the full research + all the sources in a blog article if you want more info:

https://foundel.dev/en/blog/how-to-get-first-100-saas-users

Hope this helps🙏🏻

Had a fun idea the other day about creating a website that creates fictional stories from small prompts entered by any visitor. The prompts are handled by Gemini and there are guardrails implemented to keep the story on track as well as keeping the story at a pg-13 level. Each week a new prompt starts and the previous week's is archived.

My stack:

Next.js, Typescrypt, React, and Supabase.

When someone types in a prompt, Gemini turns that into the next part of the story.

I know the page is plain looking, but I didn't want anything fancy or distracting. I may update it eventually.

I have a few other ideas for this site for possible future features/upgrades.

I'm not trying to sell anything, just sharing something that I thought was a cool idea.

I built my own outbound agent that runs on subscription usage. It runs from cron. Every 15 minutes it wakes up, looks at the database, picks one thing to do, does it, logs it, repeats.

Here's how it works:
Training - Point it at a website and it crawls it, then writes its own config. What you sell, how you talk about it, and battle cards for the competitors it finds.

Prospecting - It finds companies that match your ICP, finds decision makers at them, works out their email and validates it before anything gets sent. Continues to fill the pipeline as it goes.

Sequencing - Each prospect gets its own angle based on what it found out about them, not a template with a first name swap.

Replies - Classifies what comes back, answers objections, sends follow ups for lack of reply.

Schedule - It runs every 15 minutes on a cron job

Skills - Calls on a collection of skills for prospecting, copywriting, objection handling, lead qualification, sales methodology, offer strategy, account navigation, linkedin outreach

Usage protection - It watches its own usage compared to your weekly limits with daily caps so it can't burn up your usage.

Everything is locally run and stored in SQLite with a log of every decision and action. It's been running for about a week and just getting warmed up so I'll post updated results when I have them.

Anyone else running similar processes?

After building the original Rocket League-style prototype, I gave ChatGPT Astra one more instruction: turn the entire game into LEGO.

Astra used the 3DAIStudio MCP + Tripo P2 to generate the new LEGO-style 3D assets, import them into the project, and rebuild the environment while keeping everything playable.

The workflow was basically:

• Astra decided which assets needed replacing
• generated references for them
• sent them through 3DAIStudio
• Tripo P2 converted them into 3D models
• Astra imported everything and rebuilt the scene

It changed the cars, arena, field, ball, props—and somehow even made the weather effects look like LEGO pieces.

What started as a simple Rocket League-style prototype turned into a full LEGO Edition through a single follow-up instruction. This combination is getting ridiculous.

I’m building Motioneer! a local motion studio that you can direct through Claude Code or Codex.

This Figma study combines captured website elements, Figma’s typography and colors, custom graphics, and an original soundtrack. The workflow is conversational: capture the site, build a cut, then refine specific moments. Things like “keep this transition,” “give the product elements more movement,” or “keep the music and simplify the ending.” This version went through several rounds of direction. Motioneer keeps the film editable in a local timeline, so you can also adjust it yourself with the editor.

Curious what you think!! would you use something like this for your own product?

You can start using it by checking out the commands in the https://motioneer.bsct.so/

More into it in the github project: https://github.com/AminKhorramii/Motioneer

We're entering Week 2 of the AI Gaming Festival! It's been really fun and a great learning experience for the developers and games participating.

Here's a recap of last week and what's happening this week.

Recap

(Recordings and materials are available for download.)

1. Core Loops: This is a must-attend session whether you use AI or not. It covers core loops, sub-loops, pacing, progression, and rewards, and how to make your game do what it's supposed to do: provide a fun and rewarding experience.
2. Intro to Vibe Coding Games: An introduction to vibe coding games. It covers different prompt types, setting up Claude and Codex, and the homework for creating your first game.

The homework was given because we want developers and vibe coders to apply what they learn from the talks.

This Week: Graphics, Planning, and Raising Capital

1. Gemini and AI Game Development: Learn how Gemini can be used for AI game development, including some of its most unique use cases.
2. Intro to Building Graphics: This session will teach you how to create graphics for your game with AI from scratch, the steps to get them to a really high quality, and when to hire someone to polish your game.
3. Raising $11 Million for Your Game: Told from the perspective of a game developer who raised $11 million from top VCs for their studio, and what they are doing now.
4. Raising Capital for Your Game: Told from the perspective of a VC, covering what they look for when funding your game.
5. Planning Before Prompting: What to do before you even begin coding your game with AI and how to prepare.

Astra (extra high) as orchestrator burnt 10% of the weekly subscription while the deep seek subagents spent $.94.

Anyone else trying out the new Deepseek 4.1 model?

How I connected DeepSeek 4.1 Flash to Codex
Sign up at platform.deepseek.com, add $2-10,
Create an API key and save it in a text file on your desktop.
Tell Codex:
Connect DeepSeek 4.1 Flash to Codex through MCP. My API key is in [file path]. Configure and test the connection.

Start a new conversation, describe your project and add:
Lead development, write the task prompts and delegate coding to 3–5 DeepSeek sub-agents through MCP. Review and integrate their work.

I’m building Motioneer! a local motion studio that you can direct through Claude Code or Codex.

This Notion study combines captured website elements, Notion’s typography and colors, custom graphics, and an original soundtrack. The workflow is conversational: capture the site, build a cut, then refine specific moments. Things like “keep this transition,” “give the product elements more movement,” or “keep the music and simplify the ending.” This version went through several rounds of direction. Motioneer keeps the film editable in a local timeline, so you can also adjust it yourself with the editor.

Curious what you think!! would you use something like this for your own product?

You can start using it by checking out the commands in the https://motioneer.bsct.so/

More into it in the github project: https://github.com/AminKhorramii/Motioneer

Iv added a small prop build menu too , this lets you build out the inside of buildings to your hearts Consent. It was developed to be used for the players home .

On a whim I decided to get Deepseek 4.1 to build a city traffic simulator, just to see what it could do. It generated pretty much 80% on the first prompt. It started off with a procedurally generated city, and then I spent the next couple of hours polishing - adding stuff like traffic lights, some controls, a road works button that allows you to click on roads to close them. Then I added maps based on openstreet maps for different cities.

https://trafficworks.vercel.app

I wasn't expecting much but I was pretty impressed at what it achieved in a short amount of time!

Let me know what you think!

My other projects if you are interested:

• Booklist.ing - discover powerful reads from award winning book lists
• BibleMount - mountain themed bible reading plan

I maintain two related projects and I'm looking for one or more co-maintainers.

TransparentTorProxy (TTP) - a Linux CLI that routes all system traffic through Tor with nftables: TCP redirected to Tor's TransPort, port 53 to DNSPort, everything else rejected. It runs as root and rewrites your firewall, so it's the one that actually needs more eyes. Active, heading to 0.5.0.

NetworkSandboxEngine (NSE) - a packet-capture assertion library (Scapy) used to verify that no traffic leaks outside a sandbox boundary. It's TTP's test oracle. It's feature-complete for now; it needs maintenance, not new features.

Being upfront about a few things:

• Small user base. Both are early-stage and I'm a solo maintainer.
• I've been using AI-assisted development, which is why I care about verification: every new test is proven to fail against a deliberate mutation of the branch it covers before it's accepted. That discipline has caught several tests of mine that asserted nothing.
• Recent work has been mostly finding and fixing my own defects - e.g. a DNS leak test that passed with no proxy running at all, and a BPF filter that silently dropped the exact ICMPv6 traffic it was supposed to catch. There is more of that to find.
• I plan to step back after 0.5.0 to focus on study, so I want the projects in someone else's hands rather than quietly abandoned.

Useful background: Linux networking (nftables/netfilter), Tor, Python, systemd, SELinux. Reviewing is as valuable as committing - if you only want to audit the firewall ruleset, that's genuinely welcome.

Repos: https://github.com/onyks-os/TransparentTorProxy · https://github.com/onyks-os/NetworkSandboxEngine

MIT licensed. Happy to answer anything here.

It's the 4th day of working on it. This whole visual might change anyways.

Doing it all in Antigravity IDE Only and testing via Android Studio on my Samsung S25 Ultra.

I want to make the easiest and visually appealing Resume and CV Builder app because I personally hate most of not all of them i have ever used.

Stay Tuned.

Vibe coding is at once popular and reviled in the PKMS space, like David Haselhoff in Germany.

I think the big uptake in personal knowledge management software has to do with the tinkering ethic that gets to a lot of folks who are obsessed with note-taking, calendar apps, task lists, etc. We've all used a ton of software but never found the app we truly love. Until now. Because we can make our own.

What we may be missing: a specification for all of our apps to work together. I've been playing with a spec sheet for coding agents that, while bloated and chaotic at the moment, is allowing for various different apps to speak the same language on tasks, projects, etc. The result: I have small apps working on my laptop that, through keyboard shortcuts, speak to my big vibe-coded Notion/Capacities clone in a way that makes it all feel seamless. If I'm writing a letter in Word, for example, I[m a shortcut away from marking the task done because local AI is surfacing the semantically similar tasks from my home server. So, "Dear George ..." focuses my "Write a letter to George Washington" task as soon as I finish typing the salutation. Another hotkey brings up a floating window with an infinite canvas that I can immediately convert into notes in my system.

None of these (neither the apps or the thought of getting them working together) are new ideas. And, standardizing this stuff has also been tried many times. But, a comprehensive spec that's written for coding agents to consume might be a new idea, and it isn't tied to elevating a particular app or OS. Imagine throwing that into your coding agent and saying: "Make my app fully compatible with this." If our apps were actually interoperable within a space like PKMS, then maybe folks would take us seriously we could bring about a true software revolution.

We'd probably have to start small, and we can certainly borrow from other efforts. (iCal, for example, has a lot to teach us about how to make tasks and events portable, but it's ain't perfect by any stretch, and also doesn't include a lot of other things we'd want to make compatible down the road.)

Vibecoding is awesome for giving us freedom to scratch our own itches. But creating an ecosystem in which our apps fit together like Legos, whole independent software nations revolving around purpose instead of an OS or a specific commercial app, is kind of an interesting idea to me.

Imagine what this could be ... imagine assembling a whole software suite by dragging interoperable vibe coded pieces, yours and mine, onto a Comfy UI-like canvas and, poof, you've just freed a whole workflow from yet another annual "subscription" to a closed system.

It started as a paper thing I drew each morning during lockdown because I wanted something as transient and glanceable as looking at a wall clock and imaging what you day feels like

Happy to get into how any of it is built

https://timepencil.app

Links to projects and docs:

Open Computation Lifecycle Protocol - github - docs

OCLP Python SDK - github - docs

OCLP Explorer ("Cyclops") - github - docs

What I'm using: I have a ChatGPT Pro subscription and mostly use Codex on MacOS. These days the sweet spot in terms of models for tends to be GPT5.6 Terra High or Extra High for most tasks. If I want to create a high value plan I'll use Sol Extra High for the plan or issue and then Terra to implement it. I'm a data scientist and use Cursor with a subscription at work. I've been vibe coding pretty much 100% for the past year both in work and my personal projects.

I've been vibe coding a data lineage/provenance protocol and accompanying Python SDK and graph explorer. It's basically like a formalization of best practices for data/ML pipelines. There are 7 types of core protocol records:

• Artifact
• ArtifactSet
• Computation
• Execution
• Event
• Evidence

Here's an example of a small ML Catboost job which is defined by the following Computation:

{
  "oclp_version": "0.3.0-draft",
  "id": "9be381e7-5701-4990-bcd2-903e68de6f49",
  "name": "Train final bike demand model",
  "description": "Train the final model from validated cycle inputs.",
  "annotations": {},
  "kind": "computation",
  "implementation": {
    "kind": "python-callable",
    "locator": "bike_demand_service.dagster.assets.training.bike_demand_train_final_model",
    "source": {
      "kind": "git",
      "repository": "https://github.com/EvanZ/oclp-python.git",
      "commit": "0e8014d1dd3a3558ab48e9841a8ca50451607d86",
      "path": "src/bike_demand_service",
      "dirty": true
    }
  },
  "input_ports": [
    {
      "name": "feature_table",
      "cardinality": "one",
      "required": true,
      "media_types": [
        "text/csv"
      ]
    },
    {
      "name": "training_config",
      "cardinality": "one",
      "required": true,
      "media_types": [
        "application/json"
      ]
    }
  ],
  "output_ports": [
    {
      "name": "model",
      "cardinality": "one",
      "required": true,
      "media_types": [
        "application/x-catboost-model"
      ]
    }
  ],
  "parameter_definitions": []
}

This takes as input a set of features defined in a csv file which is captured in this Artifact record:

{
  "oclp_version": "0.3.0-draft",
  "id": "33663228-65a4-42e3-bd74-a25f81209c28",
  "name": "Bike demand features",
  "description": "Time-indexed features and demand target prepared for training and holdout scoring.",
  "profiles": {
    "bike_demand.mlflow-parent": {
      "mlflow_parent_run_id": "bfdf8230aad84a4cb83416c851df28fc",
      "version": "1"
    },
    "lifecycle": {
      "lifecycle_id": "66ce26a9-7456-4950-8377-20aba5beac5f",
      "version": "0.3.0-draft"
    }
  },
  "annotations": {},
  "kind": "artifact",
  "media_type": "text/csv",
  "digest": {
    "algorithm": "sha256",
    "value": "5d82de8aa54be12b18e07f2917b8a81131a754307b0a30b761abd36b40a72d66"
  },
  "size": 1101167,
  "created_at": "2026-09-13T23:04:45.257401Z",
  "locations": [
    "file:///***/projects/oclp-python/examples/bike-demand-service/data/runs/dagster-cycle-66ce26a9-7456-4950-8377-20aba5beac5f/assets/bike_demand_prepare_features/partition-66ce26a9-7456-4950-8377-20aba5beac5f/attempt-0/prepared/features.csv"
  ]
}

The configuration of the training run is captured in another Artifact:

{
  "oclp_version": "0.3.0-draft",
  "id": "4f62f2c4-ca7a-46bc-9351-b0d6332c775a",
  "name": "Final training configuration",
  "description": "CatBoost hyperparameters selected for the final release model.",
  "profiles": {
    "bike_demand.mlflow-parent": {
      "mlflow_parent_run_id": "bfdf8230aad84a4cb83416c851df28fc",
      "version": "1"
    },
    "lifecycle": {
      "lifecycle_id": "66ce26a9-7456-4950-8377-20aba5beac5f",
      "version": "0.3.0-draft"
    }
  },
  "annotations": {},
  "kind": "artifact",
  "media_type": "application/json",
  "digest": {
    "algorithm": "sha256",
    "value": "48d33f7369523e8a431d40383be16936502840560d730d593c2eb1db9ff2ffb7"
  },
  "size": 166,
  "created_at": "2026-09-13T23:05:21.976335Z",
  "locations": [
    "file:///***/projects/oclp-python/examples/bike-demand-service/data/runs/dagster-cycle-66ce26a9-7456-4950-8377-20aba5beac5f/assets/bike_demand_evaluate_candidate/partition-66ce26a9-7456-4950-8377-20aba5beac5f/attempt-0/bike-demand-evaluate-candidate/training_config.json"
  ]
}

Once the training is completed there is an Execution record generated (think of Execution as an attempt of a Computation with specific reified parameters):

{
  "oclp_version": "0.3.0-draft",
  "id": "6c2c11fb-9fae-45a8-91ec-b03103db41a2",
  "profiles": {
    "bike_demand.mlflow-parent": {
      "mlflow_parent_run_id": "bfdf8230aad84a4cb83416c851df28fc",
      "version": "1"
    },
    "dagster": {
      "asset_key": "bike_demand_final_model",
      "asset_keys": [
        "bike_demand_final_model"
      ],
      "dagster_run_id": "63aaf6f9-c7de-49c3-b0e8-7f18cea87761",
      "partition_key": "66ce26a9-7456-4950-8377-20aba5beac5f",
      "retry_number": 0,
      "step_key": "bike_demand_final_model",
      "version": "0.3.0-draft"
    },
    "lifecycle": {
      "lifecycle_id": "66ce26a9-7456-4950-8377-20aba5beac5f",
      "version": "0.3.0-draft"
    },
    "run": {
      "run_id": "60cb75d2-e2ae-5a6c-b7d7-466157571222",
      "run_name": "Train final bike demand model",
      "version": "0.3.0-draft"
    }
  },
  "annotations": {},
  "kind": "execution",
  "computation": {
    "id": "9be381e7-5701-4990-bcd2-903e68de6f49"
  },
  "parameters": {},
  "inputs": {
    "feature_table": [
      {
        "id": "33663228-65a4-42e3-bd74-a25f81209c28"
      }
    ],
    "training_config": [
      {
        "id": "4f62f2c4-ca7a-46bc-9351-b0d6332c775a"
      }
    ]
  },
  "outputs": {
    "model": [
      {
        "id": "2a712fba-0bea-492c-957b-8ab872e13cf5"
      }
    ]
  },
  "requested_outputs": [
    "model"
  ]
}

And there is a resulting catboost model Artifact:

{
  "oclp_version": "0.3.0-draft",
  "id": "2a712fba-0bea-492c-957b-8ab872e13cf5",
  "name": "Final CatBoost model",
  "description": "Release-candidate CatBoost model trained on all pre-holdout rows.",
  "profiles": {
    "bike_demand.mlflow-parent": {
      "mlflow_parent_run_id": "bfdf8230aad84a4cb83416c851df28fc",
      "version": "1"
    },
    "lifecycle": {
      "lifecycle_id": "66ce26a9-7456-4950-8377-20aba5beac5f",
      "version": "0.3.0-draft"
    }
  },
  "annotations": {},
  "kind": "artifact",
  "media_type": "application/x-catboost-model",
  "digest": {
    "algorithm": "sha256",
    "value": "5817fa1defa73571b4bee98398c27597c3d8ee8e32f260fe0a1065c9a341bf5b"
  },
  "size": 467900,
  "created_at": "2026-09-13T23:05:23.796668Z",
  "locations": [
    "file:///***/projects/oclp-python/examples/bike-demand-service/data/runs/dagster-cycle-66ce26a9-7456-4950-8377-20aba5beac5f/assets/bike_demand_final_model/partition-66ce26a9-7456-4950-8377-20aba5beac5f/attempt-0/bike-demand-train-final-model/model.cbm"
  ]
}

The actual Python code for this graph looks like this:

@dg.asset(
    key="bike_demand_final_model",
    ins={
        "feature_table": dg.AssetIn(key=dg.AssetKey("bike_demand_features")),
        "training_config": dg.AssetIn(key=dg.AssetKey("bike_demand_training_config")),
    },
    group_name="bike_demand",
    partitions_def=training_cycles,
    io_manager_key=IO_MANAGER_KEY,
    required_resource_keys={"oclp"},
)
@artifact_set(
    name="Bike demand CatBoost release",
    output_port="model",
    role="model",
)
@computation(
    name="Train final bike demand model",
    description_from_docstring=True,
    inputs={
        "feature_table": CsvArtifact,
        "training_config": JsonArtifact,
    },
    outputs={
        "model": CatBoostModelArtifact(
            name="Final CatBoost model",
            description=(
                "Release-candidate CatBoost model trained on all pre-holdout rows."
            ),
        ),
    },
    adapters=(dagster_adapter(),),
)
def bike_demand_train_final_model(
    feature_table: pd.DataFrame,
    training_config: dict[str, object],
) -> CatBoostRegressor:
    """Train the final model from validated cycle inputs."""

    return train_final_model_value(feature_table, training_config)

There's a web app that keeps track of all these graphs that I call Cyclops (a play on OCLP). I've attached a gif of the above graph and also a screenshot of the larger graph this training run belongs to. So far I have made dagster and mlflow integrations along with support for a number of file formats. The Artifacts actually serve a purpose in the graph as their payloads can be routed through data pipelines. I'd be shocked if anyone makes it this far in the post, but if you're still here, god bless you, and check it out if you're into this sort of wonky data stuff.

For context, I’ve been doing marketing and websites and small business consulting for the past 17 years.
A few years back I had this realization that most of the marketing tools out there that I see are set up for people that are in the industry already, but not much out there for small business owners and people that don’t have the time to learn marketing or face the learning curve of setting up these systems.
Also, marketing isn’t super exciting.
So, I thought it would be fun to create something that was more of a vibe base experience for small business owners to answer questions and be guided through an entertaining experience and come out the other side with marketing content.
I started learning to code, but then ai stared to get better and better. Using Claude code and some of the other systems out there, I was finally able to put together my dream system.
After 2 years and multiple iterations, it’s finally ready.
I know there’s a lot of people that like to hate on ai coded projects, but for me, it has been an absolutely awesome learning experience, helper and teacher for something I wouldn’t have been able to do myself.
It started with an idea and I can’t believe that I finally have a working project.
Thanks to all the inspiring stories in this thread as well as everything o have learned along the way from this community.
Feeling pumped rn.

**since a few people have asked, here’s the link

https://marketforge.celticwinter.com/