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Sonata - platform for self-improving internal tools

Hacker News

Sonata - platform for self-improving internal tools

Hey everyone, I'm Cameron and I'm building Sonata, which is a knowledge graph for internal LLM tools. My thinking is that internal tools are going to become more and more prevalent and complicated with LLMs as it becomes easier to generate high quality software. A core piece of infrastructure that we'll need for this to work is a high quality knowledge graph i.e. a place where all of the information, preferences, goals, terminology etc lives. I think the future will be that at every company: 1. There will be a load of really great internal tools (e.g. Klarna replacing Salesforce and Workday with internal tools) 2. Your single sign on for these tools will come with this knowledge graph. Another way to think about this is that current "AI Workers" are always on their first day in the job, but with Sonata they'll start on day 100 and only get better with time. Would love any feedback but more importantly I'd love to demo anyone the product and get you using it. Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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