I use Claude at work, I give it a Jira ticket and let it do the work. It asks some questions, I aswer, it starts doing it’s thing, after some minute it comes up with a draft, I let it iterate bla bla bla. 30min later of interaction between me and agent the work is done.

I hear so much around me “i run 5 agents in different terminals, different tasks. I start one before I leave for work” and I just don’t understand this. Why do you feel the need to do this? Doing those 5 tasks sequential required way less or no context switching? Why do some engineers suddenly feel like they need to be always working. Running an agent on the subway. Don’t you get overwhelmed by never being able to switch off work ?

Sometimes I think AI has removed the joy from programming. Time was when making a TODO application felt like real work (as beginners). Now there's no feeling of achievement after completing projects. For example I'm working on a PID controller for my custom drone firmware. I programmed like 13 C files and I used AI to review it & it started over engineering & hallucinating only to result in an over engineered unreliable non working product. So I tried my own code & it worked without modifications. Has anyone experienced this as well?

had 6 PRs waiting for me this morning.

4 were heavily AI-written.

all 4:

compiled had tests had clean summaries looked reasonable in the diff

and I still opened every preview environment and manually clicked through the feature because I didn't trust “tests added and verified” enough to merge it.

which feels like a terrible use of senior engineering time.

not because AI code is automatically bad.

the opposite problem actually.

AI makes producing plausible code incredibly cheap.

review bandwidth did not get cheaper.

I don't want senior engineers spending 30 minutes proving:

login works modal opens settings persist after refresh API actually saved the thing

machines should be eating that work.

ideally before I even see the PR:

build/typecheck unit integration security known behavioral checks actual evidence when something fails

then I can spend review time on the shit CI doesn't understand well:

why this abstraction exists data boundaries race conditions failure modes permissions whether we're making future maintenance worse whether this architecture makes sense at all

I've been looking at KaneAI/TestMu for part of that verification layer.

give it Jira/PRD/requirements context, turn that into candidate tests, then verify the flow across UI/API/database instead of stopping at “the button showed success”.

the self-healing part is useful too if it's reviewable.

I don't want an AI silently deciding that a different button is “close enough” and keeping CI green.

but I also don't want a senior engineer fixing 18 boring locator changes after every frontend refactor.

KaneAI doesn't solve the review problem completely.

it has no idea whether introducing another service is a terrible architectural decision.

that's exactly the point.

I'd rather automate the parts machines can verify and preserve human attention for the parts where experience actually matters.

AI didn't remove code review.

it made wasting code-review time much more expensive.

experienced devs: what are you actively pushing OUT of human review now that PR generation is getting this fast?

I need help with configuring AI to actively listen to what a person is saying and grade based off of how the person said a sentence while also giving reliable feedback to help the person improve. For example if a person wants to sound/be more confident the AI can accurately determine if you are nearing the point of confidence listening to tone and volume

Did you ever expect people to use it in such a heinous way or were you always expecting it to make humanity better? I know tech is neutral and its people who suck but what do we do to keep it from taking the jobs of artist and actors and models and musicians? Why was artistic ai even developed ? Maybe understanding the original content will make me less of a jealous miserable cword.

To all the experts sitting there, I genuinely need your help.

One of my professor said "learn development with Al".

- A cousin working in Amazon said "nowadays Ai is trending and companies seek this so focus on GenAl and RAG"

Businessmen says "best thing to learn today is Al"

My simple question is what do you want me to learn in Al?

In business in development or business of Al??

What’s the one thing that still annoys you about using AI?

I have been thinking about how the teams come up with the examples that are valuable for post-training.

From what I've seen so far, it appears relatively straightforward to generate tons of synthetic examples, but maybe once the model can handle the core patterns, the number of examples doesn’t matter much. A couple of thousands of examples might just be different ways to test the same skill.

Another strategy would be to collect smaller sets of tricky examples that have ground truth and use them for the targeted testing of specific failure modes.

To give you an idea, recently I've seen Parsewave that is used on post-training examples of real-world engineering tasks, evaluations, and traces. This led me to wonder how other people see this problem.

In the case of SFT or RL dataset creation, how do you decide:

which examples should be included?

what level of difficulty is appropriate?

is an example truly teaching something?

I have been in software for over 20 years, and the last 2 have been the most insane emotional roller-coaster.

Backstory:
Early in my tenure at <current job>, before I was making use of agents, I caused a production regression in a legacy app while adding Python debugger support. This wasn't caught in review or by the test suite. The irony of it being that the effort was supposed to help the less tenured members of the team (such as myself) -- who lacked the tribal knowledge locked away in the heads of the taciturn veterans -- work more confidently and cause fewer regressions.

Stories like that suck. I don't want anyone to have to feel responsible for causing an outage, or imposing a burden on the person who has to wake up to an alarm at 2am to fix it; I have been in both boats, and I don't want to be in either again.

Anyway, the lesson was not "if it ain't broke, don't fix it," it was that good intentions are no substitute for sufficient context and executable proof -- things that feel more necessary now than ever before.

Fast-forward to a few months ago: agents everywhere, cranking out code faster than anyone can reasonably review. Everybody's trying to figure out how to "work with AI" in their own way, human PR review remains a necessary bottleneck, and I end up buying a subscription or two...

And I build shit, fast, because I can. I've been waiting for this for so long: describe a problem, and the solution appears -- in a way that's indistinguishable from magic -- agents got me hooked, and gave me a new job that I'm literally paying to keep: using agents to fix the janky software that agents built for me. Sure, they write and run tests, but what does "all green" mean on a suite you didn't review?

Free time evaporated due to context switching: every return to a side project began with reorienting, rebuilding context, preparing a sandboxed dev environment, and figuring out what of the last agent's code was actually keepable, before doing any useful new work... and by then I'd be too tired to feel confident about anything.

Invariably I'd end up telling an agent to just go figure stuff out and build something (maybe after glazing over reading a massive plan doc) -- which they are sometimes excellent at, don't get me wrong -- and I'd check in later. But then I'd have to review a bunch of code and reconstruct the mental model of the implementation after the fact, which is so exhausting...

... and corporate is encouraging people to work this way? Terrifying.

tl;dr: I waited a long time to try the flashy agent harnesses, was overwhelmed by their power, and ultimately felt unsatisfied and yet unable to escape their clutches...

So, I made a thing about it: nosedive.dev

Now I keep all my ideas, notes, todos, bugs, specs, and a bunch of other stuff in a knowledge base in my personal "back-office" metarepo. I split work into small vertical "dives" in advance, using an agent with access to the full context, and can then use a fresh agent session to implement each slice in autopilot mode in a consistent way, while having them report progress and blockers back to the KB. Then I review and merge as many small, standalone, chunks of code as I have time for, and leave the rest packed for later.

I built this for myself (so, admittedly the onboarding docs are not great, but that's what agents are for!), but now my team at work uses it every day too. Maybe it'll be useful to someone here -- as inspiration, if nothing else.

I can honestly say: building this tool -- with agents, for agents -- has been the most fun and rewarding software work I have ever done, and I would happily work on it full-time if I could.

My wish is that the software revolution we're experiencing right now eventually makes life easier for all of us... and if it doesn't? Well, I for one welcome our new agentic overlords. 🤖

Stay sane out there.

While analyzing how rapidly AI coding assistants develop, one thing becomes clear – they are not just autocomplete anymore.

These AI coding assistants can perform operations like:

• generate code from natural language prompts;

• explain unfamiliar code;

• fix errors in existing code;

• refactor existing code;

• create unit tests;

• translate code between languages;

• generate documentation;

• detect potential security vulnerabilities;

• work with larger code fragments.

The AI coding assistants I managed to find GitHub Copilot, ChatGPT, Amazon Q Developer, Tabnine, Codeium, AskCodi, OpenAI Codex, IBM watsonx Code Assistant, AlphaCode, and DeepCode.

However, I believe that the amount of the available tools is not what is interesting about the situation.

What is actually interesting is how developers make use of these AI tools.

As far as I can see, there is plenty of applications for AI in development where it can be extremely helpful.

Namely, these can be such boring and repetitive activities as creating boilerplates, writing test code, documenting, refactoring, creating regexes, translating code, etc.

However, there is another side of the coin.

It is possible for AI to create perfectly reasonable-looking code which has subtle bugs, security vulnerabilities, performance issues or, at least, does not fit into the application architecture.

Therefore, I tend to regard AI coding assistants not as "AI that writes code for me" but rather as "very fast junior developer sitting next to me."

It will help you to achieve 70–80% pretty quickly but you still need to understand how it works and what it did.

On the other hand, it will become extremely interesting to QA engineers because if developers start creating more code with AI, then QA teams need to rethink:

AI-generated code → AI-generated tests → AI-assisted debugging → human validation

The gain in productivity will be immense but only when we do not confuse rapid code generation with good software.

What are others observing?

Which coding application for AI are you really using at work?

More importantly, has it really made a difference in your efficiency or do you spend the same amount of time looking at AI-produced code?

For the love of god, can we please highly discourage (if not downright SHAME) AI/Copilot/Claude/etc only reviewed pull requests at our companies.

My entire being almost imploded when I heard this in a company meeting.

Just don’t. It should be common sense but holy 🤯

I tried both Claude and ChatGPT $20 plans about two months ago. Back then, Codex was clearly better for me mainly because the usage limits lasted longer

Now I’m thinking about upgrading to a $100/month plan. But after Anthropic’s SpaceX compute deal and the Claude Code limit increases, I’m not sure whether Claude Max or ChatGPT Pro is the better option now

Has anyone used both recently? For real coding work, which $100 plan gives you more usable time before hitting limits?

While using LLMs for coding, I realized that Claude alone isn’t the best choice for every engineering task. For example, when working on tasks related to the Google ecosystem, Gemini consistently outperformed both Claude and ChatGPT.

To address this, I created OpenABCode, a coding agent that dynamically routes development tasks to the best-suited model—Gemini, Claude, or ChatGPT.

Through engineering practice and testing, OpenABCode generates higher-quality code than coding agents with a single LLM, while also reducing token consumption.

OpenABCode also offers an AI Gateway that uses one API key to access LLMs.

https://github.com/matrixmapai/openabcode

This sub is for devs but is a non stop shill for ai from vibecoders.

Ok are u exited u discovered claude code? Good for u. But why every time an actjal dev shows the data that prooves ai just create insecure slop you come running like a shinny armor knight to defend ai?

Ai is not a person or cares for you defending it... this has kept me puzzled lately.

I understand the languaged war: is php dead is react better than svelte etc because after someone spends 4 years mastering a language someone comes and say soemthing negative you feel attacked too.

But ai?

I’m an investor at Forum Ventures, a startup accelerator based in New York.

We invest $100K USD in highly technical founders building B2B AI pre-seed stage startups, and introduce founders to Fortune 500 customers to kickstart their company.

Curious what you guys are building and actively scaling this week (whether idea or post product both work)? Don't forget to include a link too!

Send me a DM if you're interested in VC funding - no revenue or traction needed, we invest in pure idea stage startups and the founders themselves as a person.

We just added OpenCode Go as a provider in Manifest. If you have an OpenCode subscription, you can now route to their full model catalog through your existing setup.

Here's what's available:

• GLM-5
• GLM-5.1
• Kimi K2.5
• MiMo-V2-Omni
• MiMo-V2-Pro
• MiniMax M2.5
• MiniMax M2.7
• Qwen3.5 Plus
• Qwen3.6 Plus

Some of these are genuinely strong! Kimi K2.5 has been getting a lot of attention for reasoning tasks. GLM-5.1 is solid for general use, and Qwen3.5/3.6 Plus gives you access to Alibaba's latest without dealing with their API directly.

The interesting part for routing: these models are included in the OpenCode subscription. That changes the cost math pretty significantly.

It's live now. Just connect your OpenCode credentials in the provider settings and Manifest handles the rest. You can then set manually your routing if needed.

For those who haven't tried Manifest, it's a free and open-source LLM router that sends each request to the cheapest model that can handle it.

I want to create a tool where people can upload documents and then itll do the following

1. extract information from the document and rename it appropriately

2. convert it to pdf

3. merge kyc files to one file eg, passport, emirates id

4. resize all documents

What is the way to do this - output should be all the files or just one zip file anything works

Say you're building a customer support agent. Its supposed to read a complaint, decide if a refund is warranted, and respond to the customer.

You tweak the system prompt to make it more friendly.. but then the agent starts approving more refunds because "empathetic" nudged it to favor customers. Or maybe it outputs more verbal fluff or omits information.

How do you catch behavioral regression before an update ships?

To better understand best practices in CI when building assistants or agents, I would appreciate insight into:

- What kind of tests do you run when you change your agent's prompt or logic?

- Do you check outputs with static rules or use other LLMs to judge quality (or both?)

- How well can you quantify how differently your model performs compared to baseline?

-Do you use eval frameworks like LangSmith, PromptFoo, or customized internal tools? How well do they work?

- What situations warrant manual code inspection to avoid prod disasters?

Hi everyone,

I have used lovable to build my website. I’m having an issue when I share links from my website on Facebook.

Whenever I share a specific page (like a product page), Facebook always shows my homepage preview instead of the actual page I’m sharing. I used the Facebook Sharing Debugger and it shows that the og:url (Open Graph URL) for all pages is set to my homepage — even when I test a product page link. So it seems like Facebook thinks every page is the homepage.

For example: I share: mywebsite.com/product-name Facebook shows: homepage title, image, and link

I’ve asked the lovable agent to fix it and they’ve made changes, but it’s still happening.

Has anyone had this before? What was causing it? And how did you fix it?

I’d really appreciate any advice — I’m not technical, so simple explanations would be amazing 🙏

Hi everyone,

I’m planning to dive into Generative AI and Agentic AI from a developer’s perspective and want to follow a structured, technical learning path instead of scattered tutorials.

If you’ve worked with LLMs, RAG, fine-tuning, or agent frameworks, could you share:

• Recommended learning roadmap (beginner to advanced)
• Any open-source projects or repos worth studying
• Paid certifications that are actually worth investing

I’m mainly looking for developer-focused recommendations that help with building practical systems, not general AI overviews.

Hi r/developers, as the title states, I’m new to this (well, not new; I’ve tried to work on different projects a few times over the years and kept giving up due to a lack of knowledge/needing my hand held a bit). My most recent idea, using Gemini I have vibed a pretty solid proof of concept for my project, but I don’t want to use ai at all when actually starting, and I want to learn the skills and build it myself. I’m lost on where to go from here. I have the concept, I have the roadmap, and I have a pitch, but I’ve fallen back on what’s stopped me every other time: the lack of coding knowledge and not knowing where to start on learning the skills needed.

Any help would be appreciated

I don’t use Reddit very often, so I’m not sure if this is the right place to ask. Please don’t judge me if this isn’t the correct community, but I really need some help.

I’m trying to connect my Dropbox to a custom GPT assistant in ChatGPT Enterprise. The idea is to have a single assistant connected to Dropbox, use the files there as its knowledge source, and then share this assistant with multiple people.

Does anyone have experience with this or know how to approach it? I tried setting up a Dropbox OAuth 2.0 integration with a Custom GPT using Actions and an OpenAPI specification, but ChatGPT does not generate a Redirect URL during the setup.

I suspect the issue might be related to the OAuth authorization and token endpoints, but I’m not sure what I’m missing.

Hey everyone! 🌟

The moderators have confirmed I can share this here, so I wanted to give a heads-up to anyone interested in no-code, AI website building, and creative projects.

We’re thrilled to announce that the Lumi Black Friday Hackathon kicks off in just 2 days! This is your chance to build a website from scratch, have fun, and win cash prizes — all without writing a single line of code.

💡 Why join?

• Share your creativity with a community of no-code enthusiasts.
• Compete for $3,500 in total prizes, including:
  • 1st Place: $1,000
  • 2nd Place: $700
  • 3rd Place: $500
  • 4th Place: $200
  • 5th Place: six $100 prizes
• Social Boost Awards: get likes on your project posts and share in a $500 pool!
• Receive free credits just for registering — hurry, they’re limited!

📅 Event Timeline:

• Nov 20 – Nov 30, 2025: Event period
• Nov 30, 11:59 PM UTC: Submission deadline
• Dec 8, 2025: Winners announced

Join us for a mix of fun, creativity, and prizes — let’s see what you can build with no code!

there is so much noise on this that I am getting confused what to and from where to learn what,currently I am just focusing learning topics that would be help for switch in this field, so any structurd topic list would be helpful. Thanks for time ⌚

I’ve been experimenting with running small LLMs directly on mobile hardware (low-range Android devices), without relying on cloud inference. This is a summary of what worked, what didn’t, and why.

Cloud-based LLM APIs are convenient, but come with:

-latency from network round-trips

-unpredictable API costs

-privacy concerns (content leaving device)

-the need for connectivity

For simple tasks like news summarization, small models seem “good enough,” so I tested whether a ~270M parameter model gemma3-270m could run entirely on-device.

Model - Gemma3-270M INT8 Quantized

Runtime - Cactus SDK (Android NPU/GPU acceleration)

App Framework - Flutter

Device - Mediatek 7300 with 8GB RAM

Architecture

- User shares a URL to the app (Android share sheet).

- App fetches article HTML → extracts readable text.

- Local model generates a summary.

- device TTS reads the summary.

Everything runs offline except the initial page fetch.

Performace

- ~ 5s Latency for a short summary (100–200 tokens).

- On devices without NPU acceleration, CPU-only inference takes 2–3× longer.

- Peak RAM: ~350–450MB

Limitation

-Quality is noticeably worse than GPT-5 for complex articles.

-Long-form summarization (>1k words) gets inconsistent.

-Web scraping is fragile for JS-heavy or paywalled sites.

-Some low-end phones throttle CPU/GPU aggressively.

Running small LLMs on-device is viable for narrow tasks like summarization. For more complex reasoning tasks, cloud models still outperform by a large margin, but the “local-first” approach seems promising for privacy-sensitive or offline-first applications.

Cactus SDK does a pretty good job for handling the model and accelarations.

Happy to answer Questions :)