Sri Santh

880 posts

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Sri Santh

Sri Santh

@srisanth2004

Founder of @Hipocap | AI Researcher and Consultant

Earth Se unió Ocak 2022
117 Siguiendo120 Seguidores
Garry Tan
Garry Tan@garrytan·
Just launched GBrain v0.8.0 If you have it installed, you can just ask your Claw/Hermes to upgrade to the latest GBrain and we'll automatically ask if you want to install your Voice WebRTC endpoint and Twilio number It's a true mega brain-trip to talk to your agent directly.
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Rohit
Rohit@rxhit05·
As a founder, where are you marketing??
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MicroLaunch
MicroLaunch@MicroLaunchHQ·
What are you building this weekend?
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Sri Santh
Sri Santh@srisanth2004·
See this is how my graph evoles over time instead of being flat like obsidian... Imagine in obsidian or any other tools... You will create a graph only once spending so much tokens on it. Instead, you can use this which will generate initial graph without LLM needed and evolves over the time based on the question you can to it... Imagine having a Librarian inside the DB who help to identify and arrange things in a optimized manner... Thats what stixDB has... github.com/Pr0fe5s0r/Stix…
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Blake Emal
Blake Emal@heyblake·
Drop your project URL Let’s drive some traffic
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Sri Santh
Sri Santh@srisanth2004·
ok, i got a idea to work on a agentic memory system... Accidentally, created a version of a $6.5 Million dollar idea and made it Open Source - lol😀 github.com/Pr0fe5s0r/Stix… i failed at competitor analysis... my budget is $20 claude subscription anyways 🚶
Nishkarsh@contextkingceo

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

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Ali Abdaal
Ali Abdaal@AliAbdaal·
building out the llm second brain inspired by all the @karpathy shenanigans over the past few days
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Mari
Mari@Tech_girlll·
first time you wrote hello world - what language did you use?
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Kritika
Kritika@kritikakodes·
Why is everything getting Open Source?😂
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by
by@beyoumf·
without drugs and alcohol. what’s the best way to escape reality?
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kriti
kriti@draft_ofkritika·
can I ask a dumb question… if everyone uses AI… what makes someone better than others
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Dear Self.
Dear Self.@Dearme2_·
at your lowest, who do you talk to?
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Sakshi Sugandhi
Sakshi Sugandhi@SakshiSugandhi·
"AI makes everyone a developer" is true the same way "cameras makes everyone a photographer"
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Umair Shaikh
Umair Shaikh@1Umairshaikh·
What are you building this weekend? Drop your project URL Let’s drive some traffic
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Mike Futia
Mike Futia@mikefutia·
Self-improving Claude Code skills are f*cking ridiculous 🤯 One loop → 10 test runs, scored against an eval, prompt rewritten, retested, winner kept. All inside Claude Code. Perfect for DTC brands and agencies who have built Claude Code skills but the output is still a coin flip: great sometimes, completely unusable the rest. If you've been editing your skill prompts based on vibes, re-reading every single output, adjusting one line at a time, running it again, and hoping this version is finally the one that sticks... This loop eliminates the entire cycle: → You define 3-5 binary eval criteria for your skill → Claude runs the skill 10 times with varied inputs → A separate evaluator scores every output against your criteria → It identifies the most common failure patterns across all runs → Rewrites the skill prompt to fix exactly what's failing → Retests and keeps the winner → Repeats until the score plateaus No manual prompt tweaking. No reviewing every output by hand. No "it worked that one time but I can't reproduce it." What you get: → A skill prompt that's been pressure-tested across 50+ automated runs → A scored improvement log showing exactly what changed and why → Eval criteria you can reuse every time you update the skill → A method that works on any skill: hooks, briefs, ad copy, scripts, reports Same loop AI labs use to improve their own models, applied to your creative workflow. I put together a full playbook showing how to set up the eval, the exact Claude Code prompt for the improvement loop, and starter eval criteria for the 5 most common DTC creative skills. Want the playbook for free? > Like this post > Comment "SKILL" And I'll send it over (must be following so I can DM)
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Sri Santh
Sri Santh@srisanth2004·
@shivi1026 Git: local and Github: cloud - that the main difference
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Shiviii
Shiviii@shivi1026·
Git and GitHub are not the same. What's the difference?
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