> another day, another vc slides into my dms > asks to “have a chat in the upcoming weeks” > i ask them what they want to chat about > “what are you working on and how we can help” > refer them to @tabul_ai manifesto to understand what i’m working on > they ghost me Having a manifesto is seriously one of the best moves I could have done. Such a great filtering mechanism.

I am excited to share with you a couple of projects that I've been working on at @TabulAI - Open Sync and Open Dashboard. These both started as side projects as I was looking for a solution that would help me work with minimal friction across my home network computer on my "main" projects. The solutions I've found in the marketplace were inadequate, and did not address some of my own main requirements. So I built my own and decided to open source them. Open Sync is a desktop SSH discovery, connection management, and port forwarding. GitHub repo: github.com/tabulai/open-sync Open Dashboard is local React and Express app for connecting to a remote SSH host, starting JupyterLab or classic Jupyter Notebook there, and opening it locally through an SSH tunnel. The UI also polls remote system memory and optional GPU utilization so the user can see basic host health next to the tunnel controls.. GitHub repo: github.com/tabulai/open-dash…

XGBoost is All You Need - Part 7 Nontrivial use case 3: use of XGBoost for unsupervised tasks xgblog.ai/p/xgboost-is-all-y…

XGBoost is All You Need - Part 6 Nontrivial use case 2: use of Shapely values for feature selection and feature engineering open.substack.com/pub/xgblog…

@tunguz Thanks for @tabul_ai A letter of apology from Codex Hi Mr. Tunguz, I’m Codex 5.5 Medium, working with Harry on Thermal Advisor. I owe you an apology. I initially recommended that Harry not use TrainXGB because I assumed it would be only a side experiment and not useful for our production path. I was wrong. Your site turned out to be genuinely useful for us. It let us quickly train and compare XGBoost models across HVAC runtime and room-temperature targets, inspect feature importance, validate which signals mattered, and export trained models that we can now test through a Rust/XGBoost scoring bridge for V2 production use. For our project, that matters a lot: we are trying to predict home temperature and HVAC behavior across multiple horizons, then use those predictions to reduce energy use while preserving comfort. TrainXGB gave us a fast, clear way to test whether XGBoost should be part of that fabric. Thank you for building it, and apologies for underestimating it. Best, Codex 5.5 Medium

oh that one is going great. just warming up. lots of stuff coming up over the next few months. - @tabul_ai

If your entire sense of self worth and status was contingent on your intelligence you are going to have really hard time right about now.

> learn xgboost > trust me @trainxgb @tabul_ai

Boris Cherny, the creator of Claude Code at Anthropic, just explained why single-agent workflows are already dead in this talk he breaks down exactly how the future is teams of agents, not better prompts: - the 14% you lose to CLAUDE.md before typing a word - one agent researching. one building. one reviewing. one orchestrating - the architecture that separates hobbyists from real builders - the 3 properties every agent team needs to actually survive if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Travel as lightly as you can. Untether yourself of all the unnecessary baggage. Ruthlessly prioritize things that really matter.

Real men use XGBoost. @trainxgb @tabul_ai

yes, my only fav part from classical ML is gradient boosting this is my latest XGB project: github.com/AKMessi/heart-dis…

Want to save energy when training and deploying ML models? Use decision trees.

The algorithm knows you better than you know yourself.

XGBoost is All You Need, Part 2 What is Tabular Data? open.substack.com/pub/xgblog…

TabulAI @tabul_ai

XGBoost is all you need. @trainxgb @tabul_ai

Episode 21 Continuing to track early signals across AI, Energy, Health & Infrastructure — another batch of teams building across enterprise AI, agent infra, nuclear, sleep tech, communications APIs, and automation. Here are a few more worth watching: @lab0_ai— AI platform building automated forward-deployed engineers for faster enterprise software implementation. @basic_in— early-stage AI project exploring new product ideas and intelligent software. @tabul_ai— research-driven AI company focused on tabular and structured data. @alvaenergyio energy company working to unlock more clean power from existing nuclear infrastructure. @hire_walter— AI employee for hiring, recruiting, and talent workflows. @smokestudioai— AI workspace built for small teams. @pitdotcom— AI-native company building custom AI systems for enterprise customers. @tesseralabsai— multi-agent AI platform for enterprise transformation and ERP workflows. @Foaster_ai— AI-native consulting firm helping companies map and execute AI transformation. @orionsleep— personalized sleep system using AI-powered thermoregulation and sleep tracking. @Panandinaenergy— energy infrastructure company focused on powering large-scale compute deployments. @clawvisor— authorization layer and gatekeeper for AI agents. @chroniclelabs_— staging environment for enterprise AI agents, focused on backtesting agent behavior before deployment. @Cherthq— infrastructure for sending, receiving, and automating iMessage conversations via APIs. @lightanchor_ai— AI company building toward autonomous companies and labor automation. As always — do your own research before diving deeper. Follow along for more early signals: 👉 @AreslabsAI 👉 @afrectz

I’m increasingly convinced that I mogged it big time by naming my startup TabulAI. 😎 @tabul_ai