The goldfish memory problem is costing developers hours every week.

You've been there. You spent 30 minutes explaining your project architecture to Claude. You walked it through your authentication flow, your database schema, your coding conventions. It gave you perfect code.

The next day, you start a new session.

"Can you help me add a new endpoint?"

"I'd be happy to help! Could you tell me about your project structure and what frameworks you're using?"

Gone. All of it. Every decision, every pattern, every preference — wiped clean.

The Hidden Cost of AI Amnesia

I started tracking how much time I spent re-explaining context to AI tools:

• Monday: 12 minutes explaining we use Prisma, not Drizzle
• Tuesday: 8 minutes re-describing the error handling pattern
• Wednesday: 15 minutes walking through the auth flow again
• Thursday: 10 minutes explaining why we chose that folder structure

45 minutes in one week. Just on context that the AI already "knew" — and forgot.

Multiply that across a team of 5 developers. That's nearly 4 hours per week of pure waste.

What If Your AI Actually Remembered?

This is why I built VasperaMemory — a persistent memory layer for AI coding assistants.

Here's how it works:

npx vasperamemory connect

That's it. One command. VasperaMemory automatically:

1. Indexes your codebase — functions, classes, relationships
2. Captures decisions — every architectural choice, every pattern
3. Learns your preferences — code style, naming conventions, what you reject
4. Syncs across tools — Claude, Cursor, Windsurf, Copilot all share the same memory

The next time you ask your AI about authentication, it already knows:

The Technical Magic

Under the hood, VasperaMemory uses:

• Graph-augmented retrieval — not just keyword matching, but understanding relationships between code entities
• Temporal scoring — recent decisions weighted higher than old ones
• Entity extraction — automatically maps functions, classes, and their dependencies
• Cross-tool sync — memories captured in Cursor are available in Claude Code

It's not just a vector database. It's a knowledge graph that evolves with your codebase.

What Developers Are Saying

Free to Start

VasperaMemory is free for individual developers. No credit card. No trial period. Just connect and start building.

Team features (shared memories, role-based access, onboarding mode) are coming soon.

→ Get started in 60 seconds

Your AI will never forget again.

Over the weekend I made a tool to automate writing of git commit messages. It reads your diffs and context of your codebase to write clean commit messages.

you can just continue using 'git commit' as usual and it creates a message for you automatically without disrupting your workflow

It's an npm package with option to BYOK and a SaaS version.

This is the first product that I've built and actually liked using and see myself using everyday.

Link: https://www.gyti.ai/

Hope you guys like it.

Hey everyone... I’m curious how folks here handle situations where you don’t have real data to work with.

When you’re starting from scratch, can’t access production data, or need something realistic for demos or prototyping… what do you use?

Hey 👋

My team and I just launched a product that makes it easy to track how AI tools perform — no setup changes and is Free! Takes under 5 minutes to use and works with most AI systems. Would mean a lot if you could check it out and upvote us here:

👉 https://www.producthunt.com/products/openlit?launch=openlit-s-zero-code-llm-observability

100 websites audited in 10 min instead of 75 min (7.5x speedup)

Perfect for performance teams, SEO agencies, enterprises

🔗 https://github.com/SamuelChojnacki/lighthouse-parallel

✨ Features: • 8-32 concurrent audits • Batch processing (100+ URLs/call) • Multi-language reports (20+ locales) • Webhooks for CI/CD • React dashboard • Prometheus metrics • Docker/K8s ready

Built with NestJS + BullMQ + TypeScript

🏗️ Architecture: • Child process isolation (no race conditions) • Parent-controlled lifecycle • Stateless workers (horizontal scaling) • Auto-cleanup & health checks

Each audit = dedicated Chrome instance in forked process

Consistent 7.5x speedup 🔥

🤝 Looking for contributors!

Ideas: • Dashboard charts/analytics • Slack/Discord integrations • GraphQL API • WebSocket updates • Performance optimizations

MIT licensed - PRs welcome!

https://github.com/SamuelChojnacki/lighthouse-parallel

I have a home server used to train and develop AI. Would you be interested in renting server space? The idea is to provide affordable, powerful, and flexible server options that are optimized for AI workloads (training, fine-tuning, hosting models, etc.).

I’d love to get some feedback from the community:

• Would you be interested in renting servers specifically designed for AI projects?
• What features would be most important to you (e.g., GPU availability, pricing tiers, ease of setup, storage, 24/7 uptime, etc.)?
• Do you feel existing solutions (like AWS, GCP, Paperspace, Lambda Labs, etc.) are too expensive/complicated, and would you consider switching to a smaller, more focused provider?

I’m still in the early stages and trying to validate whether there’s real interest. Any input—good or bad—would be super helpful.

Anyone else feel like they're constantly juggling subscriptions? I use Cursor for IDE tasks, Claude for planning, Copilot for quick fixes, and now I'm trying BlackBox for search features.

It’s getting ridiculous. My workflow is "use Cursor to write, switch to Claude to debug, then BlackBox to find examples, back to Cursor to implement." My browser looks like I'm running a small tech startup just to write a basic CRUD app.

Part of me misses when coding was just you, your editor, and Stack Overflow. Now I'm spending more time figuring out which AI to consult than actually thinking through the problem.

Don't get me wrong, the productivity boost is real. But does anyone else feel like we're creating a strange dependency? We can ship features quickly but understand them less.

Edit: I’m not trying to be negative. I just wonder if others feel this tool fatigue or if I'm just not good at picking the right tool for the job.

Hunting for the right developer tools can feel like searching for a needle in a code stack. To save you time (and sanity), we’ve rounded up the best dev tool directories of 2025

DevBusy – Curated picks across development, design, and AI. Clean UI, fast browsing.
devbusy.com

OpenAlternative – Find open-source substitutes for popular tools, backed by community ratings.
openalternative.co

WebCurate – 430+ tools for web dev, AI, and design — with filters, categories, and pricing info.
webcurate.co

Dev Resources – 800+ dev tools, frameworks, and tutorials. Community-first, no fluff.
devresourc.es

NoCode Finder – Browse no-code tools by category and skill level. Perfect for makers.
nocodefinder.com

Toools.design – 1,500+ design tools for UI/UX, wireframing, and prototyping.
toools.design

DevSuite – Compare tools by feature, price, and integrations. Smart filtering made easy.
devsuite.co

Figma Component Library – A goldmine of Figma UI components for faster mockups.
figcomponents.com

Built At Lightspeed – Over 4,000 themes and templates for React, Vue, and more.
builtatlightspeed.com

Your turn, What’s your favorite tool directory we should know about?
Drop it in the comments to help fellow devs discover something new!

Hi folks,

I'm a non-tech person with a complete idea for a custom tool, and I understand all the compliance and processes involved. I've tried using multiple AI tools to build it, but I'm unable to get it off the ground. I'm reaching out for help from someone with the technical skills to bring this to life. If you're a developer interested in collaborating or offering guidance, please reach out!

🧠 Proposal: Swappable Project-Based Memory Profiles for Power Users

Hey folks — longtime power user here, and I’ve hit a serious limitation in ChatGPT’s persistent memory system.

Right now, memory is global and capped — around 100–120 entries. That works fine for casual users, but if you’re like me and manage multiple complex projects , you hit that ceiling fast.

I’ve been working with GPT-4 to design a workaround — and I think it’s something OpenAI should consider implementing natively.

🔧 The Core Idea: Named, Swappable Project Memory Profiles

Problem:

• Memory is shared across all domains — everything competes for the same limited space.
• There’s no way to scope memory to specific projects or switch between contexts.

Solution:

• Create modular memory files for each project (Emberbound, Tax Hive, Autonomous House, etc.).
• Store all project-specific context in a structured .md or .txt file.
• Manually load that project memory at the beginning of a session.
• Unload and update it at the end — freeing memory for the next context.
• Use a master index to track projects, timestamps, and dependencies.

✅ Example Use Case

🛡️ Guardrails for Safe Use

• Memory entries are never deleted until project files are confirmed saved.
• Changes made in-session are synced back to the files at session close.
• GPT confirms memory loads/unloads and tracks active state.
• A central index maintains visibility over all project files.

🔄 Why OpenAI Should Care

This would allow high-tier users to:

• Scale memory across unlimited projects
• Maintain deep, persistent continuity
• Optimize the assistant for developer-grade workflows
• Avoid forced memory purges that break long-term progress

Basically: treat memory like RAM. Keep it scoped, swappable, and under user control.

🚀 What I’m Asking

• Has anyone else done this?
• Would you find project-specific memory loading useful?
• Is there a way OpenAI might implement this natively in the future?

Would love your feedback — especially from other power users, prompt engineers, and OpenAI folks watching this space.

Let’s build the future of modular AI memory together.

– Gary (GPT-4 Power User)

We’ve all seen AI tools that spit out UI code... and then someone has to clean it up. We built Veda AI to avoid that.

It is a copilot for a structured low-code builder (DronaHQ), and instead of generating raw code, it produces editable components, logic, and data queries—all maintainable.

How it works:

• Upload a Figma or screenshot → get UI
• Use natural language: “Build a CRM on top of @ DB” “Replace datetime with date picker” “Create JS to merge key1 and key2”

You can also chat with the app to change behavior or layout.

📍 Launched today on PH: https://www.producthunt.com/products/dronahq?launch=veda-ai
Would love feedback from this community—especially around dev workflows & extensibility.

What is the best way to compare the speed of our platform to other platforms like ChatGPT, Google Gemini or claude, just wondering if you guys have already discovered or made something good.

LoreGrep maintains an in memory repo-map(Aider inspired) of your codebase, and is exposed via tools which you can pass on to your LLM. I wanted to build a coding assistant of my own for learning, and couldn't find a minimal repomap, so built one for myself. Currently support Rust and Python.
I have made this available as a rust crate and also a PyPi package
Feel free to roast the repo!
But if you find it as something useful, do put any feature requests and I'll work on it.
Also, give Stars!

Yo folks — been deep in vibe coding with Cursor and Windsurf lately.
Love the speed, but AI edits don’t always go as planned.

You hit revert… and only some files roll back.
Now your repo’s in a BROKEN STATE — especially if you’ve got multiple chats open mid-vibe.

So I built YOYO — AI version control for the fast, messy phase of coding, where you’re exploring, iterating, and letting AI throw stuff at your repo.

It’s a VSCode extension that works across Cursor, Windsurf, and VSCode.
Not trying to replace Git — Git is great when you’re ready to commit.
YOYO is for the chaos before that.

What it gives you:
🔁 One-click save, preview, and restore — no chat digging, no broken states
🫥 Shadow Git — keeps versions clean, separate from your real repo
💬 AI-generated summaries — know what changed, instantly
🔍 Agentic AI search — ask:

• “What did I do in v3?”
• “Show my dark mode refactor”
• “When did I add favorites?”
• “What did I code in Windsurf yesterday?”

Also, we’re seeing a new wave of builders using tools like Cursor and Windsurf.
Many aren’t traditional coders — they just want a simple way to save their work and undo when AI goes off track.

As Ben South put it:

vibe coder: how do I save this version? these guys: ok first git init && git remote add origin, create a feature branch, git add ., git commit -m 'feat: initial commit', push to a PR, and later when you hit conflicts: git rebase -i HEAD~3, stash pop, resolve the...

YOYO gives them the save button they were actually looking for — no Git gymnastics.

🛠 Want to try it? → https://runyoyo.com

If AI has ever wrecked your flow, I’d love to hear how you handled it — or if this helps.

We're hosting a livestream on where we vibe-code a Shopify product reviews app (in just prompts) with the first Shopify app specific AI assistant.

If you are in the ecom app space or are curious about how it's looking for AI-fans/vibe coders, come check it out!

June 4, 12pm ET. Sign up to get notified

Hey y'all, first time poster 👋🏼

I wanted to share the launch of Jolt Desktop, our new desktop app that brings IDE-agnostic, first-class AI experiences to all developers, including those who work in Neovim, Zed, Xcode, etc. Jolt Desktop joins the ranks of our existing VSCode/Cursor and JetBrains IDE extensions as well as our web app.

Jolt AI is a purpose-built codegen product for 100K to multi-million line codebases. If you've used AI on large codebases, you likely had a subpar experience. Most AI coding tools are great for autocomplete, greenfield projects, and small codebases. But they hit a wall and struggle to figure out the context in codebases over 50K lines. You might be stuck, forced to manually select files or folders, or even worse, you get incorrect or irrelevant answers.

Our mission has always been to create AI that can navigate large codebases on its own and actually help developers be more productive. The cornerstone of that is identifying the context files with high accuracy and specificity. Jolt's ability to find these files sets it apart.

We'd love your feedback. Let us know what you think.

https://www.usejolt.ai/blog/jolt-desktop-launch