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What Spiderbrain actually does. Spiderbrain runs over a project directory and produces a sibling folder. It is never committed to the repo, external, the way the spider's synganglion sits outside the legs it controls. The folder contains a master graph file, synganglion.json, and one cluster directory per functional area of the project. Eight legs on perform.digital: shell, database, pigi, leads, pages, products, blogs, admin. Each one mapped to a leg of the synganglion.
Introducing Spiderbrain V3
Massive AI Hallucination and Cost Reduction (Tokens) for Claude, Cursor and other AI coding agents.
Key problems with AI coding assistants today:
1. Memory issues
They forget something about your project every session. Agent memory gets compressed repeatedly and information is lost during every transition.
2. As projects grow larger, agents hallucinate and invent file names, functions, imports, and dependencies that do not exist. Massive amounts of tokens are then spent fixing errors introduced by the agents themselves.
3. AI tools keep burning money rediscovering what they already knew last week. Sessions do not persist, memory disappears, and developers must constantly re-explain context. Testing, debugging, and bug fixing consume huge amounts of tokens.
And these are only a few of the problems.
Spiderbrain reduces:
Over 80% hallucination | 95% cross-session rediscovery costs | 77% fewer tool calls | Up to 70% faster workflows | & more
Steps to install and test:
1. Copy the GitHub URL : https://lnkd.in/gAfHfGw9
2. Paste the URL into your AI coding agent and point it to your project folder with text: "Use Spiderbrain on my project"
That's it, it'll quickly create a folder it uses to assign scores and weights to your files without effecting your project.
What it does:
Spiderbrain is built around two core concepts:
1. Inspired by how a spider's brain works, it creates a parallel intelligence layer alongside your project without modifying the project itself.
2. It generates multiple contextual variables and builds a K-nearest-neighbour cluster model using an 8-leg topology. Each node carries a webscore value that determines information traversal and routing.
Key Benefits:
1. #Reducesredundancy across AI sessions, teammates, and tools by creating one canonical project brain instead of repeated rediscovery.
2. #Reduceshallucinations by giving agents a structural ground-truth reference before generating file names, APIs, dependencies, or versions.
3. Improves #routingefficiency by providing orchestrators with a project-level amplitude signal based on mass, recency, and master phase.
4. Improves #inferenceutilisation by collapsing the rediscovery phase that consumes most session tokens.
5. Lowers operational costs through compounded efficiency gains across development workflows.
More info: https://lnkd.in/gstAwjCJ (#SHA hash available on #blockchain)
Honest disclaimers:
1. Recently releases and not tested on enterprise scale projects yet, initial test displayed the bigger the dataset the faster the algorithm works.
2. I created it for #claudecode over time 6 others are also planned including #gemini #cursor #openai & #mistral
3. The git repo is source available on #github, #challenges are in place. Source - available under BUSL 1.1, free for personal use.
Can you customize how it defines clusters, or is it inferred automatically from the codebase?
About SpiderBrain V3 - Reduce 80-95% AI Cost on Product Hunt
“Massive Tokens, Cost and Hallucination Reduction ”
SpiderBrain V3 - Reduce 80-95% AI Cost was submitted on Product Hunt and earned 2 upvotes and 4 comments, placing #105 on the daily leaderboard. What Spiderbrain actually does. Spiderbrain runs over a project directory and produces a sibling folder. It is never committed to the repo, external, the way the spider's synganglion sits outside the legs it controls. The folder contains a master graph file, synganglion.json, and one cluster directory per functional area of the project. Eight legs on perform.digital: shell, database, pigi, leads, pages, products, blogs, admin. Each one mapped to a leg of the synganglion.
SpiderBrain V3 - Reduce 80-95% AI Cost was featured in SaaS (42.6k followers), GitHub (41.3k followers), Marketing automation (4k followers) and Vibe coding (520 followers) on Product Hunt. Together, these topics include over 72.1k products, making this a competitive space to launch in.
Who hunted SpiderBrain V3 - Reduce 80-95% AI Cost?
SpiderBrain V3 - Reduce 80-95% AI Cost was hunted by abhishek srivastava. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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abhishek srivastava