🤝 Top Collaborators
.png&w=640&q=75)
🤓 Latest Submissions
Regression Whisperer
Development teams lose their hard-won debugging knowledge every single day. When an engineer spends an hour tracking down a MongoNetworkError caused by an IP whitelist issue, or a JsonWebTokenError from a misconfigured refresh flow, that knowledge almost never gets documented — not because it isn't valuable, but because writing it up properly (a clear title, a root cause explanation, structured fix steps, searchable tags) takes real effort on top of an already-solved problem. So the fix lives only in that one developer's head, or buried in a Slack thread nobody will ever search again. Six months later, a teammate hits the exact same error and starts the whole investigation from zero. Regression Whisperer turns that friction into a 30-second action. A developer pastes a stack trace and a short note on what fixed it; Gemini distills it into a structured, tagged lesson and saves it instantly — no manual write-up required. The next time a similar failure appears, a developer can search by stack trace or description and get scored matches against the team's own accumulated fixes, ranked by error-type and tag overlap. This is retrieval that's faster and more relevant than a general web search, because it's built entirely from the team's own history. Beyond individual lookups, Regression Whisperer is a multi-tenant SaaS platform: companies sign up, and every team's data is fully isolated. A company-wide Analytics dashboard turns raw error activity into insight — top error types, languages, a 14-day trend, resolution rate, average time-to-resolution, and a "Knowledge Base Growth" chart showing the team's collective debugging memory compounding over time. Engineering managers get visibility into what's actually costing their team time, not just anecdotes. We also removed friction in two more ways: a voice input option lets a developer describe a fix out loud instead of typing while context-switching mid-debug, and a CSV bulk-upload (with a downloadable template) lets a team migrate an existing spreadsheet or wiki of known issues into the system in one pass — turning a static, un-searchable document into a live, growing knowledge base from day one. Future roadmap: an IDE extension for automatic diagnostic/terminal-error capture (removing even the 30-second manual step for the most common errors), and per-project organization within a company account for teams working across multiple codebases.
27 Sep 2026
mindX Augmentic Intelligence
The design of self-improving intelligence has long been a central challenge in artificial intelligence (AI) research. While contemporary machine learning techniques have achieved unprecedented performance across domains such as vision, natural language processing, and game playing, they remain fundamentally constrained by static architectures and externally imposed objectives [Russell & Norvig, 2020]. The quest for open-ended, autonomous, and self-constructive intelligence has driven theorists and practitioners alike to explore approaches that extend beyond conventional paradigms. Two particularly influential contributions in this lineage are the Gödel Machine, introduced by Schmidhuber [2003; 2009], and its conceptual extension, the Darwin–Gödel Machine. These frameworks draw on deep theoretical insights from Gödel’s incompleteness theorems and Darwinian evolution to propose mechanisms by which a system might engage in recursive self-modification, thereby transcending the limitations of fixed architectures. However, both models remain largely theoretical, with limited practical instantiation. In the mindX Augmentic Intellgence core folder agint.py is designed as an agnostic intelligence to act as a traditional Gödel Machine consisting of: A formal axiomatic system describing its own software, hardware, and utility function. A proof searcher that attempts to find formal proofs that specific self-modifications will increase its expected utility. A self-rewrite mechanism that executes such modifications once proofs are discovered. This design theoretically guarantees a self optimised decision workflow : if the system finds a provably beneficial modification, it will implement it, thereby becoming strictly better at achieving its objectives. In this way, mindX will include in its own design extrapolation from existing agents into a system deciding from action which agent / tool to use and / or procedure to build. mindX is an implementation of a Darwin-Godel machine.
21 Sep 2025
PromptStack
PromptStack is an AI-powered platform that generates full-stack SaaS applications from simple prompts. Unlike tools that only create static pages, it builds real backend logic, APIs, databases, and interactive dashboards. Developers can make live updates using natural language and have complete access to the codebase, including editing, versioning, and deployment control. PromptStack empowers teams to accelerate development, streamline workflows, and fully customize their applications with ease and precision. It reduces time-to-market and enhances productivity for startups and enterprises alike.
8 Jul 2025
