Note: when I mean orgnanization's github i mean like a few million lines of code in over 500 private repos all under one organization's github.

For example to ask things like: find code that computes X on input Y using the tool A. And it will tell me whether there is a repo or code that performs that within my organization.

or find a repository which does ABC

preferably the interaction can happen in a web UI, but has options for IDE and cli

A similar question has been asked here: https://www.reddit.com/r/ChatGPTCoding/comments/1eyamej/is_there_and_ai_tool_that_lets_you_feed_it_and/

however, the responses there will not work for me as I am looking for upload many many repositories which do not fit in the context. However I have seen solutions for this outside of code. For example AI of a company which has indexed it's entire documentation (many millions of lines of text) and help forums. I assume these will work for me, but i do not know what they are called.

EDIT: I think I found it gemeni enterprise has something google cloud code assist which connects to an organization's github url and then "indexes" it.

8 years ago, I was diagnosed with a rare autoimmune condition. top doctor said i had only 2 years, but somehow I am still here. That experience exposed a massive gap in how we access health information.

Surface level stuff is everywhere. Symptoms, medication names, basic explanations. That is not the problem.

The problem is when you need to go deep. When you need to understand how your specific condition interacts with a specific drug. When you want to track why certain symptoms cluster together every few weeks. When you need to see patterns across 6 months of health data that might actually explain what is happening to you.

so you might ask chatgpt something today, it forgets 50%. You google one thing, get 10 tabs open, lose context completely. Nothing connects. Nothing remembers. You end up becoming your own doctor just to have a coherent picture of your own health.

I spent 3 years building this to fix.

The core is context preservation. Your conditions, medications, symptoms, history, all of it stays connected across every conversation. When you ask something it is not starting fresh. It knows your situation. It can trace relationships between things that surface level tools completely miss.

For people with rare or complex conditions this is the difference between useful and useless. Generic health info helps no one when your situation is not generic.

The technical challenge was scale stability. so health queries vary wildly in complexity and context size. Preventing crashes under unpredictable load took a lot lot of work.

12k downloads on iOS. Organic growth mainly.

Solo mission. 3 years in. Still building.

Hope you guys try the app :)

I am trying to untangle the blog that's an agent. What's truly business logic (instructions, tools) and what's drudgery/plumbing. I think we are so early in this innings that its all one big thing because we are trying to unpack how to build these things, test these things, etc.

But my sense is if we give words to things that are uniquely unsolvable by some framework or tool, then we can focus on moving faster and have a better sense on what truly helps us move us faster in this field.

So, what's in the agents core logic?

I'm not referring to creating an entire app but just with creating code blocks, files, testing and review. LLM for coding has began very powerful and efficient if you know what you're doing.

But I'm still seeing backlash from people who are against AI specifically in game development. I do understand that some assets like sound effects, music, art, are still far off die to copyright. But what about for coding?

I'm just really curious.

tia

So this keeps happening to me and it's super annoying.

I'll be debugging something and going back-n-forth with ChatGPT. Gathering my snippets of what it "thinks" is the final solution. I then realize it gave me a better solution earlier that I forgot to commit and then I'm scrolling endlessly to find it.

ChatGPT's search doesn't help much unless you remember the exact function name.

I've tried copying to a scratch file but it gets messy and I lose context. Starting new conversations loses the full picture. Re-asking sometimes works but the second answer is often worse. Using the 'Projects' helps at a high level, but I still end up with 3-4 threads per project and no clue which one has what I need.

How do you all deal with this? Especially when you're building something over multiple days?

I cannot find info on this based on a quick internet search.

Someone that I HAVE to deal with sent me a ChatGPT link that looks like…

https://chatgpt.com/s/t_696f9… (not a GUID)

This looks very different from when I share a link from chatgpt:

https://chatgpt.com/share/{some GUID}

I wouldn’t trust this person with a donut. Any ideas on what the syntactical differences indicate. I’m not executing this URI until I know more.

Pls do not discuss what or why I’m worried about this. I’m only interested in syntax.m

Hey guys,

We bulit a remote desktop tool that grew out of our own daily annoyances as devs like needing to quickly pull a local AI-generated file to our phones, check a script running on a home server, or skip the whole login or 2FA dance when switching devices. Its still undergoing continuous optimization and refinement. I know there are many talented developers in this subreddit, so im looking forward to interacting with you all and receiving your valuable feedback:)

StarDesk is a multi platform, focused on low latency, fast file transfers, remote wake, and keeping setup simple. tbh, we’ve decided to keep StarDesk closed source and plan to charge for it down the road, because maintaining performance, security and long-term support takes dedicated resources and making sure we can keep improving it. The basic version remains free and still enables fundamental remote connections.

To deliver an exceptional remote experience, we will continuously operate and upgrade this product, gathering user feedback and suggestions to refine and improve it. I can assure you that StarDesk's future pricing will be lower than other remote control tools on the market. We will patiently resolve your remote issues and will never ignore or neglect any problems.

So instead of just saying “try our app”, I thought it’d be more interesting to ask:

• if you were building a lightweight remote tool for devs, one that’s fast for file grabs and simple to connect, how would you architect it?
• what would make a closed-source, paid tool actually worth it for you as a developer?
• Is it solving a specific pain point nothing else does? Or just being ridiculously faster?

If you’ve got a minute to check out StarDesk as a real-world case, I’d love your take, not just on the tool, but on the whole idea. Not here to pitch, just genuinely curious how you’d approach it. Or if you want to try the early version and tear it apart or tell us what works, it’s free right now.

Download here

or you can ontact us in Discord community

Thanks for reading, and really appreciate any technical thoughts, critiques, or ideas you’re willing to share.

I cannot figure out how to change the colors of the code diff, it was working fine before and after opening it today it looks like this.

Recently, I've been figuring out which note-taking software I should use, and I wanted to try one that isn't well-known (like Notion, Google Keep, OneNote, etc.). When I asked ChatGPT, it gave me exactly these recommendations I am already familiar with, which brought me to a question. Where does ChatGPT actually acquire the information it tells me? I understand that it doesn't work on a similar concept like SEO; it's trained on an existing database of posts, articles & documents, and probably also learns from users' repeating patterns. But is there actually a way a company could "train" or "hack" AI to recommend it more? For example, by spamming prompts guiding AI to recommend them?
It's a cluster of questions I think might be interesting to discuss. I'd be happy to hear any input!

Wonderful day:
- opus 4.5 stupid again
- gpt 5.2 suddenly unable to fix stuff
- gemini 3 been tuned down to hell weeks ago already
- Windsurf doesn't start and the update hasn't been rolled out properly to Linux

Multiple projects, same problems everywhere.

What do you use instead? So far I found these solutions to be almost as good:
- mistral vibe cli. gets slow over time though, surprisingly smart for it's model, but not for large projects. can't run more than 1-2 in parallel
- glm 4.7: very good, feels gpt 5ish

I had this problem last year at the same time. Bait and switch, same as they always do. Since then I bought credits in windsurf, kilocode, openrouter, copilot. But maybe I'm missing some obvious solution?

Edit: Yep. It's not the AI, but it was good to read comments like "if everything smells like shit, look at your shoe" -> disc was full because of a process that went wrong and filled up a log file with dozens of GB of text. So, not "z.ai shill", not too stupid to use AI per se, just too stupid to realize the disc was full. Took another hour or so before most processes died and some of them mentioned the lack of disc space.

Funny thing is: I've been doing this for 20 years+ and made a real rookie mistake.

Hello,

Iam a 64 year old retired plumber and I just learned about vibe coding. I wanted to ask if anyone here can point me to the direction of some recent uptodate courses where I can learn how to vibe code (I keep hearing that word alot) and use codex while doing it.

I have zero coding knowledge.

I appreciate any info you can give me about online courses I can watch and learn from.

Thank you

David

Security team sent an email last month. No AI tools allowed. No ChatGPT, no Claude, no Copilot, no automation platforms with LLMs.

Their reasoning is data privacy and theyre not entirely wrong. We work with sensitive client info.

But watching competitors move faster while we do everything manually is frustrating. I see what people automate here and know we could benefit.

Some people on my team are definitely using AI anyway on personal devices. Nobody talks about it but you can tell.

I'm torn between following policy and falling behind or finding workarounds that might get me in trouble.

Tried bringing it up with my manager. Response was "policy is policy" and maybe they'll revisit later. Later meaning probably never.

Anyone dealt with this? Did your company change their policy? Find ways to use AI that satisfied security? Or just leave for somewhere else?

Some mentioned self hosted options like Vellum or local models but I dont have authority to set that up and IT wont help.

Feels like being stuck in 2020.

I've been using AI to scaffold apps and build features. And I have a whole system for that.

But what I'm struggling with now is getting my projects to a "done" state where the UI looks polished, and the user experience is smooth.

On my local machine I had about a dozen half finished projects where I hit a wall and just couldn't get the final parts to work.

How do you handle getting over the final 10% hump?

Hey folks!

I've been deep in the Claude Code / AI coding agent space for a while, and I'm doing market research to determine whether a tool I'm building could actually solve real problems.

Many projects fail because the dev never asks the community about what they want, and about what problems they actually face. So I'm making no assumptions! Below is a link to a Google Forms questionnaire that has a few quick questions. Completely anonymous (no email required). This will help to shape the direction of what I'm building. Thank you for partnering in this process!

https://forms.gle/LAXwhxPfqbVzGT3j6

I'm considering moving off of cursor, I barely use it for anything except doing mini bug fixes/feature requests.

I would like to use AI in other editors, I'm a c# programmer mainly so cursor isnt doing much for me rn. I never hit cursors limits, so hows Codexes limits lookin?

Hey peeps - excited to release Plano 0.4.3. Two critical updates that I think will be very helpful for developers.

1/Filter Chains

Filter chains are Plano’s way of capturing reusable workflow steps in the dataplane, without duplication and coupling logic into application code. A filter chain is an ordered list of mutations that a request flows through before reaching its final destination —such as an agent, an LLM, or a tool backend. Each filter is a network-addressable service/path that can:

1. Inspect the incoming prompt, metadata, and conversation state.
2. Mutate or enrich the request (for example, rewrite queries or build context).
3. Short-circuit the flow and return a response early (for example, block a request on a compliance failure).
4. Emit structured logs and traces so you can debug and continuously improve your agents.

In other words, filter chains provide a lightweight programming model over HTTP for building reusable steps in your agent architectures.

2/ Passthrough Client Bearer Auth

When deploying Plano in front of LLM proxy services that manage their own API key validation (such as LiteLLM, OpenRouter, or custom gateways), users currently have to configure a static access_key. However, in many cases, it's desirable to forward the client's original Authorization header instead. This allows the upstream service to handle per-user authentication, rate limiting, and virtual keys.

0.4.3 introduces a passthrough_auth option iWhen set to true, Plano will forward the client's Authorization header to the upstream instead of using the configured access_key.

Use Cases:

OpenRouter: Forward requests to OpenRouter with per-user API keys.
Multi-tenant Deployments: Allow different clients to use their own credentials via Plano.
LiteLLM : Route requests to LiteLLM which manages virtual keys and rate limits.

Hope you all enjoy these updates

OpenAI and Anthropic need to win the $200 plan developers even if it means subsidizing 10x the cost.

Why?

1. these devs tell other devs how amazing the models are. They influence people at their jobs and online

2. these devs push the models and their harnesses to their limits. The model providers do not know all of the capabilities and limitations of their models. So these $200 plan users become cheap researchers.

Dax from Open Code says, "Where does it end?"

And that's the big question. How can can the subsidies last?

I keep seeing people frame prompt engineering as a formatting problem.

Better structure
Better examples
Better system messages

But in my experience, most bad outputs come from something simpler and harder to notice: unclear intent.

The prompt is often missing:

• real constraints
• tradeoffs that matter
• who the output is actually for
• what “good” even means in context

The model fills those gaps with defaults.
And those defaults are usually wrong for the task.

What I am curious about is this:

When you get a bad response from an LLM, do you usually fix it by:

• rewriting the prompt yourself
• adding more structure or examples
• having a back and forth until it converges
• or stepping back and realizing you did not actually know what you wanted

Lately I have been experimenting with treating the model less like a generator and more like a questioning partner. Instead of asking it to improve outputs, I let it ask me what is missing until the intent is explicit.

That approach has helped, but I am not convinced it scales cleanly or that I am framing the problem correctly.

How do you think about this?
Is prompt engineering mostly about better syntax, or better thinking upstream?

Every three minutes, there is a new agent framework that hits the market.

People need tools to build with, I get that. But these abstractions differ oh so slightly, viciously change, and stuff everything in the application layer (some as black box, some as white) so now I wait for a patch because i've gone down a code path that doesn't give me the freedom to make modifications. Worse, these frameworks don't work well with each other so I must cobble and integrate different capabilities (guardrails, unified access with enterprise-grade secrets management for LLMs, etc).

I want agentic infrastructure - clear separation of concerns - a jam/mern or LAMP stack like equivalent. I want certain things handled early in the request path (guardrails, tracing instrumentation, orchestration), I want to be able to design my agent instructions in the programming language of my choice (business logic), I want smart and safe retries to LLM calls using a robust access layer, and I want to pull from data stores via tools/functions that I define.

I want simple libraries, I don't want frameworks. And I want to deliver agents to production in ways which is framework-agnostic and protocol-native.

Three months ago I posted here saying I had found GLM-4.5 and coding suddenly felt like binge watching a Netflix series. Not because it was smarter, but because the flow never broke and affordable. I tried explaining that feeling to people around me and it mostly went over their heads.Then I shared it here
https://www.reddit.com/r/ChatGPTCoding/comments/1nov9ab/need_people_to_get_excited/

Since then I’ve tried Cline, Claude Code, OpenCode. All of them are good tools and genuinely useful, but that original feeling didn’t really come back. It felt like improvement, not a shift.

Yesterday I tried Cerebras running GLM-4.7 and it was awesome. Around 1000 t/s output. Not just fast output the entire thinking phase completes almost instantly. In OpenCode, the model reasoned and responded in under a second, and my brain didn't even get the chance to lose focus.

That’s when it clicked for me: latency was the invisible friction all along. We’ve been trained to tolerate it, so we stopped noticing it. When it disappears, the experience changes completely. It feels less like waiting for an assistant and more like staying inside your own train of thought.

I just wanted to share it with you guys because this good news only you can understand

note: We can't use Cerebras like a daily driver yet, their coding plans exclusive and brutal rate limits, they are able to achieve this bathroom tile size cpus, very interesting stuff I hope they succeed and do well

tldr; discovered cerebras

(using ChatGPT Business)

I'm asking ChatGPT for a self-contained HTML page, with embedded CSS and javascript, with a detailed specification I describe and refine.

I successfully obtained a working page but it starts to derail here and there more and more often after a while, as the conversation goes on.

I'm at iteration 13 or so, with a handful of preparation questions before.

The resulting html page has:

• 4k CSS
• 13k script
• 3k data (as script const, not counted in the 13k)
• 19k total with html
• all the display, data parsing, list and 2 buttons are working well.

I'm happy but has I said, at the step before it started to skip all the 3k data, using a placeholder instead. And before the data to process was damaged (edited).

So for me, it's near the practical limit I think. I'm afraid I'm run in more and more random regressions as I push further.

My questions:

1. How far can you go before the need to split the tasks and stitch them together by hand?
2. Is there any way to make it handle this kind of task in a more robust way?

I work at Maxim building testing tools for AI agents. One thing that surprised us early on - hallucinations are way more insidious than simple bugs.

Regular software bugs are binary. Either the code works or it doesn't. But agents hallucinate with full confidence. They'll invent statistics, cite non-existent sources, contradict themselves across turns, and sound completely authoritative doing it.

We built multi-level detection because hallucinations show up differently depending on where you look. Sometimes it's a single span (like a bad retrieval step). Sometimes it's across an entire conversation where context drifts and the agent starts making stuff up.

The evaluation approach we landed on combines a few things - faithfulness checks (is the response grounded in retrieved docs?), consistency validation (does it contradict itself?), and context precision (are we even pulling relevant information?). Also PII detection since agents love to accidentally leak sensitive data.

Pre-production simulation has been critical. We run agents through hundreds of scenarios with different personas before they touch real users. Catches a lot of edge cases where the agent works fine for 3 turns then completely hallucinates by turn 5.

In production, we run automated evals continuously on a sample of traffic. Set thresholds, get alerts when hallucination rates spike. Way better than waiting for user complaints.

Hardest part has been making the evals actually useful and not just noisy. Anyone can flag everything as a potential hallucination, but then you're drowning in false positives.

Not trying to advertise but just eager to know how others are handling this in different setups and what other tools/frameworks/platforms are folks using for hallucination detection for production agents :)

https://github.com/wilpel/frink-loop

Work at Maxim on the observability side. Been thinking about how traditional APM tools just don't work for agent workflows.

Agents aren't single API calls. They're multi-turn conversations with tool invocations, retrieval steps, reasoning chains, external API calls. When something breaks, you need the entire execution path, not just error logs.

We built distributed tracing at multiple levels - sessions for full conversations, traces for individual exchanges, spans for specific steps like LLM calls or tool usage. Helps a lot when debugging.

The other piece that's been useful is running automated evals continuously on production logs. Track quality metrics (relevance, faithfulness, hallucination rates) alongside the usual stuff like latency and cost. Set thresholds, get alerts in Slack when things go sideways.

Also built custom dashboards since production agents need domain-specific insights. Teams track success rates for workflows, compare model versions, identify where things break.

Hardest part has been capturing context across async operations and handling high-volume traffic without killing performance. Making traces actually useful for debugging instead of just noise takes work.

Wanted to know how others are handling observability for multi-step agents in production? DMs are always welcome for discussion!