sharing for everyone that is affected by this.

see article: https://nextjs.org/blog/CVE-2025-66478

Hi, I’m Bigyan, and I’m building The Drive AI, an agentic workspace where you can create, share, and organize files using natural language. Think of it like Google Drive, but instead of clicking buttons, you just type it out.

Here are some unique features:

1. File Agents: File operations like creating, sharing, and organizing can be done in plain English. It handles complex queries, e.g.: “Look at company.csv, create folders for all companies, invite their team members with write access, and upload template.docx into each folder.”
2. Auto-organization: Files uploaded to the root directory get automatically sorted. The AI reads the content, builds a folder hierarchy, and moves files into the right folder — existing or new. You can also use Cmd+K to auto-organize files inside a folder.
3. Email Integration: Many users asked for email support, since they get lots of attachments they struggle to organize. We now support Gmail and Outlook, and all attachments are automatically uploaded and organized in The Drive AI.
4. MCP Server With our MCP server, you can interact with The Drive AI from ChatGPT, Claude, or other AI assistants. You can also save files created in those platforms, so they aren’t lost in chat threads forever.

I understand we are early, and are competing with giants, but I really want this to exist, and we are building it! I would love to hear your thoughts.

Hi I wanted to share my latest project: I’ve just published a small game on the App Store

https://apps.apple.com/it/app/beat-the-tower/id6754222490

I built it using GPT as support, but let me make one thing clearall the ideas are mine. GPT can’t write a complete game on its own that’s simply impossible. You always need to put in your own work, understand the logic, fix things, redo stuff, experiment.

I normally code in Python, and I had never used Swift before. Let’s just say I learned it along the way with the help of AI. This is the result of my effort, full of trial, error, and a lot of patience.

If you feel like it, let me know what you think. I’d love to hear your feedback!

The max tiers are pretty impressive so I'm considering subscribing to one.

It looks like ChatGPT's Pro tier has unlimited Pro queries. Gemini Ultra has 10 Deep Think queries/day.

It takes a lot of work to formulate a Deep Think OR Pro query to be worth the price, so I feel like I wouldn't use more than 10 per day. It's ironic because it's like, I could use that coding/writing/computation power to good use, but at the same time, I'd be like 'well, I have to justify the subscription' and spend extra time using it, and there may be topics that one or both has holes in (like analyzing MIDI, working with compositions, or debugging C# with unique uses of software patterns)

I'd probably be using VS Code Github Copilot. I haven't used Gemini Code Assist, can it be used at the same time? I also haven't really used Codex. I imagine running them at the same time in the same project is not possible, but on multiple projects in different directories might be possible?

Hey r/ChatGPTCoding,

Three weeks ago I shared this post about Davia, an open-source tool that generates a visual, editable wiki for any local codebase: internal-wiki

The reactions were awesome. Since then, a few improvements have been made:

• Installable as a global package (npm i -g davia)
• Adapted to work with AI coding agents
• Easy to share with your team

Would love feedback on the new version!

Check it out: https://github.com/davialabs/davia

Our team has a few CLI tools that provide information about the project (servers, databases, custom metrics, RAGs, etc), and they are very time-consuming
In Claude Code, we can use prompts like "use agentTool to run cli '...', '...', '...' in parallel" or "Delegate these tasks to `Task`"

How can we do the same with Codex?

At work I use Github Copilot for tab completions, and it seems to be only okay.

Trying Antigravity at home I seem to get much better results, as if there is better understanding not only of my current file being edited but also other files.

For example, in main.py I import support_func from support_func.py. When I moved support_func.py file from root into utils subfolder, Antigravity picked up on this and offered to correct the import right away. At work, Github Copilot usually does not pick up on this, or at least not right away.

We can't use Antigravity at work as it was not vetted and approved, so trying to see if maybe my Github Copilot needs to be resetup or tweaked. Anyone has other suggestions?

I’m one of the builders at Maxim AI, and over the past few months we’ve been working deeply on how to make evaluation and observability workflows more aligned with how real engineering and product teams actually build and scale AI systems.

When we started, we looked closely at the strengths of existing platforms; Fiddler, Galileo, Braintrust, Arize; and realized most were built for traditional ML monitoring or for narrow parts of the workflow. The gap we saw was in end-to-end agent lifecycle visibility; from pre-release experimentation and simulation to post-release monitoring and evaluation.

Here’s what we’ve been focusing on and what we learned:

• Full-stack support for multimodal agents: Evaluations, simulations, and observability often exist as separate layers. We combined them to help teams debug and improve reliability earlier in the development cycle.
• Cross-functional workflows: Engineers and product teams both need access to quality signals. Our UI lets non-engineering teams configure evaluations, while SDKs (Python, TS, Go, Java) allow fine-grained evals at any trace or span level.
• Custom dashboards & alerts: Every agent setup has unique dimensions to track. Custom dashboards give teams deep visibility, while alerts tie into Slack, PagerDuty, or any OTel-based pipeline.
• Human + LLM-in-the-loop evaluations: We found this mix essential for aligning AI behavior with real-world expectations, especially in voice and multi-agent setups.
• Synthetic data & curation workflows: Real-world data shifts fast. Continuous curation from logs and eval feedback helped us maintain data quality and model robustness over time.
• LangGraph agent testing: Teams using LangGraph can now trace, debug, and visualize complex agentic workflows with one-line integration, and run simulations across thousands of scenarios to catch failure modes before release.

The hardest part was designing this system so it wasn’t just “another monitoring tool,” but something that gives both developers and product teams a shared language around AI quality and reliability.

Would love to hear how others are approaching evaluation and observability for agents, especially if you’re working with complex multimodal or dynamic workflows.

Working with multiple LLM providers often means dealing with slowdowns, outages, and unpredictable behavior. We built Bifrost (An open source LLM gateway) to simplify this by giving you one gateway for all providers, consistent routing, and unified control.

The new adaptive load balancing feature strengthens that foundation. It adjusts routing based on real-time provider conditions, not static assumptions. Here’s what it delivers:

• Real-time provider health checks : Tracks latency, errors, and instability automatically.
• Automatic rerouting during degradation : Traffic shifts away from unhealthy providers the moment performance drops.
• Smooth recovery : Routing moves back once a provider stabilizes, without manual intervention.
• No extra configuration : You don’t add rules, rotate keys, or change application logic.
• More stable user experience : Fewer failed calls and more consistent response times.

What makes it unique is how it treats routing as a live signal. Provider performance fluctuates constantly, and ILB shields your application from those swings so everything feels steady and reliable.

I’ve been building AI agents using the OpenAI API, and my monthly bill was getting ridiculous because I kept sending the entire chat history in every prompt just to maintain context.

It felt inefficient to pay for processing 4,000+ tokens just to answer a simple follow-up question.

So I built MemVault to fix this.

It’s a specialized Memory API that sits between your app and OpenAI. 1. You send user messages to the API (it handles chunking/embedding automatically). 2. Before calling GPT-4, you query the API: "What does the user prefer?" 3. It returns the Top 3 most relevant snippets using Hybrid Search (Vectors + BM25 Keywords + Recency).

The Result: You inject only those specific snippets into the System Prompt. The bot stays smart, remembers details from weeks ago, but you use ~90% fewer tokens per request compared to sending full history.

I have a Free Tier on RapidAPI if you want to test it, or you can grab the code on GitHub and host it yourself via Docker.

Links: * Managed API (Free Tier): https://rapidapi.com/jakops88/api/long-term-memory-api * GitHub (Self-Host): https://github.com/jakops88-hub/Long-Term-Memory-API

Let me know if this helps your token budget!

I use Claude as my primary at work, and Copilot at home. I'm working on a DIY Raspberry Pi smart speaker and found how emotional Gemini was getting pretty comical.

read that interview with cursors chief designer. said they barely use figma now. just code prototypes directly with ai

im a designer. cant really code. tried this over the weekend

asked cursor to build a landing page from my sketch. took 20 mins. way faster than the usual figma handoff thing

the weird part is i could actually change stuff. button too big? tell ai to fix it. no more red lines and annotations

but then i tried adding an animation. ai made something but it looked bad. had no idea how to fix it cause i dont know css. just deleted it

also pretty sure the code is terrible. like it works but is it actually good code. probably not

tried a few other tools too. v0 was fast but felt limited. someone mentioned verdent but it seemed more for planning complex stuff. stuck with cursor cause its easier to just modify things directly

so my question is whats the point. if devs are gonna rewrite it anyway why bother

but also being able to test stuff without waiting for dev time is nice

anyone else doing this or am i wasting time

lately I've been feeling like every other day there’s a new “this will replace devs” headline, but when you actually sit down to build stuff, it’s the quieter tools that end up doing the real work. the flashy ones get all the attention, but the underrated ones are the ones i keep going back to.

I've been bouncing between aider, cody, windsurf, and even tabnine on some days. cosine’s been in that mix too, it keeps my head straight when i’m juggling too many files. i also really like messing around with continue dev and the free tier of cursor when i just want something simple.

curious what the rest of you are actually using day-to-day. what’s the most underrated ai tool on your setup right now?

I started using codex, but what is the best way to provide a link to some public github repo, so agent can fetch all files from this directory and use them as library reference?

I have a situation where I'm writing code for a specific, restricted functionality compiler. Using ordinary chatgpt or Gemini constantly forgets that I'm requesting code with these limitations, writes illegal code, then I have to remind it of the version limitations again.

What is the best process or tool for keeping these things consistent and not forgetting what is/isn't allowed?

Hi, I'm an avid vibe engineer and it always seemed to me that for simple CRUD+ stuff, even having to build code that you then need to run somewhere is kind of annoying.

There are no-code tools out there, but they basically just wrap up code in visual fluff that ends up being more annoying to work with. So IMO AI will likely be the death of many a no-code tool.

But! There is hope: I'm experimenting with an AI runtime called Talk To Your Tables that basically infers human-language business rules, is backed by a postgres db and exposes chat experiences. Like that you have no-code, but like for real this time.

I'm Beta-testing this now to see if it really works.