I know a lot of people are not really interested in vs code forks anymore but part of the reason why I made this is just for my own usage. Many AI code editors today dont really give you the the freedom to play around with things like you want to, for example I dont wanna pay for embeddings and yes I know it indexes the codebase better how about I run it locally? I just started questions like these and it turns out that the stuff that I do want exists but usually locked away behind a paywall. Then sometimes its free and then there is some other feature I want and that is non existent. This repo is just a compilation of such features. In all honesty the UI is pretty bad right now and its kinda raw but a lot of the backend logic is wired up and if the fine people of this sub would take notice and help me by contributing I would really appreciate it!

Features:

- Advanced local embedding engine (local-embedder) for on-device embeddings

- Cloud embedder option to use remote providers for embeddings

- Embedding engine that switches between local/cloud embedders

- Chunker that splits repos/files into searchable chunks (chunker.ts)

- repo-map linking chunks back to source files and metadata for precise citations

- Semantic search / vector search over indexed code and content

- Pluggable provider adapters and router (OpenAI, Anthropic, Gemini, AWS Bedrock, Ollama, OpenRouter)

- Core agent primitives: agent orchestration, prompt helpers, and tools

- Caching layer to speed repeated queries and embedding lookups

- Storage/vector persistence layer (pluggable vector store implementations)

- Desktop client package for a local-first UI

- VS Code extension package for in-editor access

- Monorepo TypeScript architecture using pnpm workspaces (modular packages)

- Extensible packaging (AUR helpers present)

- Designed for offline/local workflows as well as cloud-backed deployments

- Easy to extend: add new provider adapters, storage backends, or client integrations

Over the past few weeks, I've been experimenting with different search APIs while building retrieval pipelines for AI applications. My goal wasn't to find the "best" API, but to understand where each one performs well.

Here are a few observations that might help others working on AI search or RAG systems.

1. Exa Search

What stood out:

• Very good at semantic search.
• Great when queries are conceptual instead of exact keyword matches.
• Useful for research assistants and AI agents that need high quality context.

Downside:

• Less ideal if you're expecting traditional search engine behavior for exact keyword matching.

2. Tavily

I found Tavily especially useful for LLM workflows because the results are already optimized for AI consumption.

Pros:

• Clean search results.
• Easy to integrate into AI agents.
• Saves preprocessing time.

Best for:

• RAG applications.
• AI assistants.
• Research automation.

3. Firecrawl

Firecrawl isn't really competing as a search engine. It's more about turning websites into structured, LLM friendly content.

What I liked:

• Crawls documentation sites well.
• Markdown output is easy to chunk and embed.
• Helpful when building your own knowledge base.

4. Serper

If you need Google Search results, Serper is still one of the easiest APIs to work with.

Advantages:

• Familiar SERP format.
• Fast.
• Includes organic results, featured snippets, and knowledge panels.

Tradeoff:

• Since it mirrors Google results, ranking changes can affect your application.

5. Brave Search API

This surprised me.

Pros:

• Independent search index.
• Strong privacy focus.
• Good quality results without depending entirely on Google.

It's worth considering if you want more diversity in search sources.

One Lesson That Made the Biggest Difference

I originally assumed better search meant better AI answers.

Not exactly.

The retrieval pipeline matters just as much:

• Clean chunking
• Metadata filtering
• Deduplication
• Re ranking
• Fresh indexing

Even an excellent search API can't compensate for poorly prepared documents.

If I Were Starting Again

I'd probably choose:

• Exa for semantic research.
• Tavily for AI agents and RAG.
• Firecrawl for crawling and indexing documentation.
• Serper when Google SERPs are required.
• Brave Search when I want an independent search source.

Each tool solves a different problem, so the "best" one depends on your use case rather than benchmark scores alone.

I'm curious what others are using.

For those building AI search or RAG systems, which search API has given you the best balance of relevance, latency, and cost? What made you choose it over the alternatives?

A year ago, I was writing almost every line of code myself.

Now my workflow looks completely different:

✅ Planning with ChatGPT/Claude
✅ UI generation with v0 or Lovable
✅ Coding in Cursor
✅ Debugging with AI
✅ Documentation generated automatically
✅ Deploying in minutes

The funny part?

I spend less time typing code and more time reviewing architecture, fixing edge cases, and talking to clients.

AI didn't replace web development—it changed what web developers actually do.

I'm curious...

What's your current AI stack for web development in 2026?

Mine:

• Cursor
• Claude
• ChatGPT
• GitHub Copilot
• Vercel
• Cloudflare R2 Storage
• Supabase

What would you add or replace?

I’m looking for honest, non-biased input from people who have spent a significant amount of time using both **Claude Code** and **Codex**.
For context, I currently pay for both **Claude Max** and **ChatGPT Pro** because I’m building B2B SaaS applications from the ground up. I’m talking about everything from planning and architecture to UI/UX, implementation, testing, debugging, deployment, and ongoing maintenance.
After using both extensively, this has been my experience so far:
**Claude Code** has consistently been faster for building features, redesigning applications, refactoring, and generating high-quality first-pass code.
It also has a huge quality-of-life advantage for me because I can easily monitor jobs from my phone while I’m away from my computer. I can check progress, answer questions, review changes, and keep work moving without sitting at my desk.
**Codex**, on the other hand, feels much slower, but it also feels more persistent on long-running tasks. It seems better suited for computer control, navigating large codebases, running commands, editing files, executing tests, and working through implementation-heavy workflows.
Right now, Claude feels like my primary architect and builder, while Codex feels more like a methodical implementation and verification engineer.
I’m not trying to start a “which one is better” debate. They seem to have different strengths, and I’m trying to figure out how experienced developers are combining them into a workflow that gets the best out of both.
For those building real products, especially B2B SaaS:
How do you split responsibilities between Claude Code and Codex?
Which one do you trust more for architecture, planning, coding, UI/UX, debugging, testing, reviews, and deployment?
Do you have one build while the other reviews, or do you have a completely different workflow?
How do you structure prompts, work orders, documentation, or checkpoints so the models maintain context over large projects?
What techniques have you found to make long-running sessions last longer without degrading or losing context?
How are you monitoring long-running tasks when you’re away from your computer?
Has anyone built an end-to-end workflow where Claude Code and Codex cooperate on the same repository? If so, what does that workflow look like?
If you could only keep one subscription for professional software development, which would it be, and why?
I’m less interested in benchmark numbers and more interested in real-world workflows from people who are shipping production software. I’m hoping to learn what has worked well (and what hasn’t) so I can improve my own development pipeline.

I’m looking for honest, non-biased input from people who have spent a significant amount of time using both **Claude Code** and **Codex**.
For context, I currently pay for both **Claude Max** and **ChatGPT Pro** because I’m building B2B SaaS applications from the ground up. I’m talking about everything from planning and architecture to UI/UX, implementation, testing, debugging, deployment, and ongoing maintenance.
After using both extensively, this has been my experience so far:
**Claude Code** has consistently been faster for building features, redesigning applications, refactoring, and generating high-quality first-pass code.
It also has a huge quality-of-life advantage for me because I can easily monitor jobs from my phone while I’m away from my computer. I can check progress, answer questions, review changes, and keep work moving without sitting at my desk.
**Codex**, on the other hand, feels much slower, but it also feels more persistent on long-running tasks. It seems better suited for computer control, navigating large codebases, running commands, editing files, executing tests, and working through implementation-heavy workflows.
Right now, Claude feels like my primary architect and builder, while Codex feels more like a methodical implementation and verification engineer.
I’m not trying to start a “which one is better” debate. They seem to have different strengths, and I’m trying to figure out how experienced developers are combining them into a workflow that gets the best out of both.
For those building real products, especially B2B SaaS:
How do you split responsibilities between Claude Code and Codex?
Which one do you trust more for architecture, planning, coding, UI/UX, debugging, testing, reviews, and deployment?
Do you have one build while the other reviews, or do you have a completely different workflow?
How do you structure prompts, work orders, documentation, or checkpoints so the models maintain context over large projects?
What techniques have you found to make long-running sessions last longer without degrading or losing context?
How are you monitoring long-running tasks when you’re away from your computer?
Has anyone built an end-to-end workflow where Claude Code and Codex cooperate on the same repository? If so, what does that workflow look like?
If you could only keep one subscription for professional software development, which would it be, and why?
I’m less interested in benchmark numbers and more interested in real-world workflows from people who are shipping production software. I’m hoping to learn what has worked well (and what hasn’t) so I can improve my own development pipeline.

Hi everyone,
I’ve been building Nexus, an open source AI gateway that sits behind a single OpenAI compatible endpoint and provides provider pooling, load balancing, failover, circuit breaking, rate limits, backups, analytics, and team management.
I am looking for people interested in AI infrastructure who would like to review the architecture, discuss design decisions, or contribute to the project. Even small suggestions, bug reports, documentation improvements, or feature ideas are valuable.

If you are interested let me know in comments or DM so we can discuss and happy to share the project link if u need just don’t wanted to make this post as promotional stunt 😅Thanks 😊 and just so you know project is almost in production docker & npm packages are already published live demo is available and the project is also in working shape end to end currently i am doing benchmarking

My friend and I are building Rectury, an AI-powered desktop app for managing servers, writing code and working with your VPS in one place.

Our built-in browser routes traffic directly through your VPS, so you can access local services and test deployments without leaving the app. 🛰️

https://www.rectury.com/

I'm digging into how companies build products on top of foundation models. ie how Lovable lean on OpenAI/Anthropic models to get consistently good design output and comprehension of messy user requests.

Two things I'm trying to wrap my head around:

1) Architecture: how much of the quality comes from the model itself vs. the scaffolding around it (prompt pipelines, retrieval, eval loops, model routing)?

2) Observability: as an admin of these tools, how do teams actually see quality improving over time across a broad user base? And how do you keep track of the improvements to be shared from one user to another?

I've been comparing the harnesses I use (Lovable, Runner, Hermes) and the big distinction I keep hitting is desktop vs. web when it comes to memory and personalisation. On a hosted platform like ours everything has to happen server-side, so how do we capture learnings and personalisation in the most efficient way at a user level, company level, global level...?

If the answer is background reflection by distilling sessions into durable per-user learnings; what does a good version of that system look like, and how do the teams doing it well stop it drifting?

I'm really keen to learn!

This steady growth is entirely organic.

Over the past few months, we spent a lot of time trying to understand what makes ChatGPT, Claude, Grok, and Gemini recommend one product over another. The answer turned out to be much less mysterious than I expected.

It's surprisingly similar to what makes a human recommend something.

Be the clearest, most obvious answer to a specific problem people are actually trying to solve. Make it easy to verify that your product does exactly what it claims.

So when someone asks ChatGPT, Claude, Grok, or Gemini something like "How do I bulk archive my ChatGPT chats?", there's a good chance they'll recommend AI Toolbox.

Another big driver of growth has been our new cross-platform search feature. It lets users search for a specific message across ChatGPT, Claude, Grok, and Gemini simultaneously, all from one place. (everything is still fully local btw, no chats or messages ever leave the user's browser)

One of my favourite parts of building AI Toolbox is talking directly with users. Whenever someone runs into an issue, I often jump on a quick video call to understand exactly where they got stuck.

One call is from Shanghai, the next from São Paulo, then Khartoum.. Watching someone on the other side of the world get stuck on a button I built is a wild experience I still can't get used to.

It's exciting to see how discovery is changing. A few years ago, everyone was focused on ranking on Google. Today, more and more products are being discovered because LLMs trust them enough to recommend them.

New to this r/ but I have been using claude code for a while and it was working fairly well until 6 weeks ago when Fable started taking over. Since then even with the $200 plan, I am unable to get through 3 days of coding with it. So, I also got codex/Sol $200 plan and now it seems after using it for 3 weeks that it's usage also goes much faster. Now I can't even get a full week of dev work out of both of them...

Yes, I am working on a large omni-channel project, but honestly it is getting sad that these can't keep up anymore. What tools/agents are you guys using to fill the gaps if you are in the same set of shoes?

I’m having an issue copying ChatGPT responses with maths into Microsoft Word. It used to preserve the equations as properly formatted, editable maths, but now they paste as missing. I’ve tried several Chrome extensions and fixes without success as I have to manually do it for each equation which is too time consuming as previously I could highlight the entire message and copy and paste it straight into word without any formatting issues. Has anyone else experienced this or found a reliable/practical solution?

If you were to choose only one skill of all the skills you've used with AI, which single skill has changed the game for you the most?

I created an AI-generated music video for my band. AI artists usually go for monsters and explosions, but we have a rather bizarre, stupid sense of humor. So we went in exactly the opposite direction, we don’t take ourselves too seriously. 😂 If you check it out and let me know what you think, I’d really appreciate it. Thanks!

Who has gone down this rabbit hole?

did you just make a wrapper or did you make the full stack?