2 new milestones for my little SaaS @DataFast_: ✅ $3,000/day ✅ $4,000/day Growth stalled after hitting $20K MRR, but since adding the AI bot traffic feature, it’s picking up again ($24K MRR now). A good reminder to keep shipping!

I made $83,701 in June 2026. ⭐️ TrustMRR — $30K 📈 DataFast — $21K 🏴‍☠️ Ship or Die — $15K 🧑‍💻 CodeFast — $9K 🐥 Twitter — $4K ⚡️ ShipFast — $3K 🍜 Indie Page — $845 🛡️ ByeDispute — $222 🦐 SuperShrimp — $170 🧾 Zenvoice — $138 🎞️ YouTube — $116 🌱 HabitsGarden — $114 📚 WorkbookPDF — $77 💩 PoopUp — $19 ✂️ Margins: ~85% My portfolio of small bets is on track to make $1M this year, too. It's just me and AI turning my weird thoughts into startups. Keep building!

I flew to San Francisco and ended up doing all of this: 🏎️ (self) driving a Cybertruck 💳 attending @stripe sessions 🍻 drinking non alcoholic beer with @jrfarr 💰 making 100 no-VC founders do 20 squats 🤖 making my SaaS @DataFast_ AI agent first 👴🏻 meeting our indie hacking father @csallen 🤝 meeting my favorite indie hackers @jackfriks @illyism @sobedominik @marckohlbrugge @phuctm97 @ky__zo @yasser_elsaid_ @JohnONolan and more 🔼 meeting the G @rauchg CEO of V 🇰🇷 heading to Korea

Run inference over millions of records — free of SQL, and without your data ever leaving Snowflake. Here's distributed batch inference at scale ⚙️ Title: Batch Inference at Scale URL: ⚙️ Overview A capability that runs distributed inference workloads on Snowpark Container Services (SPCS) with Ray as the execution framework. Inference runs as a dedicated distributed workload, supporting both traditional models and LLMs, consolidating complex operations into a single API call. ❓ Challenges Solved Many customers, especially those migrating from non-SQL systems, need batch inference decoupled from SQL. ・This is especially true for files and unstructured data at large scale ・Rearchitecting workflows around SQL-first patterns is a heavy burden 💡 Methodology & How It Works ・The input DataFrame is materialized and written to a stage as Parquet files ・A job is provisioned on SPCS; the primary node initializes as the Ray head and replicas join as workers ・Each worker reads staged data, performs inference independently, and writes results to an output stage ・Unified API: a single run_batch() call handles both structured and unstructured data ・Multimodal support (images, audio, video); workers load weights once and reuse across batches; JobSpec controls workers and GPU allocation 🌍 Use Cases ・Nightly summarization of millions of support tickets ・Product catalog enrichment via image-to-text generation ・Information extraction from scanned PDFs, audio transcription and labeling, video classification and description BatchInferenceTask integrates with Snowflake Tasks for DAG automation, and all processing stays inside Snowflake — running large-scale inference while preserving data governance. #Snowflake# #BatchInference#