Real developers asking for AI coding tools and workflow fixes on Reddit right now, updated weekly.
Developers don't discover tools from ads - they ask other developers. Subreddits like r/ClaudeAI, r/cursor, r/ChatGPTCoding and r/ExperiencedDevs see daily posts from people hitting real limits: agents losing context, vibe-coded apps that need security review, deploy bottlenecks, token costs, coordinating multiple coding agents.
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When I use GPT 5.5 xHigh, I need 1 billion tokens per day, if it is a long day, and many agents, 2 billion tokens. Sol 5.6 High, not xHight, not Ultra, 2 billion tokens if I spread the work over multiple 5-hour limits. Reason: The goal mode is better at 5.6, and in most cases, it does the work. Example: I had to do login infrastructure last month, took a week with GPT 5.3 and 5.5, did it, but it required a lot of prompts, and in one cases it did an “internal fallback”, GPT loves fallbacks, when it was not able to get the data but got another data, well, the “another data” belongs to “another user” Then if you leave 5.3 alone you will notice it is sending sensitive info between the client and server and it should not be doing that, and so on, so, one week. Now, Sol 5.6, another task, goal, and left it working, eat the entire 5 hour limit, but it solved stuff that I even did not think we should be paying attention too, it went to solve edge cases in goal mode. That means, the half-baked stuff you can get with 5.5 in a week, can be done way better with 5.6 in a day, but … it will use the tokens for a week or half the week. My estimates now, I need 5 billion to 10 billion tokens per day to produce very high-quality code that is well tested, the code, the playwright test cases simulating real user work, the internal verifiers, the database, the edge cases, and so on. What are your token needs?
212$ in tokens inless then 2h on plan work when weekly ran out (max 20). Chat GPT, here I come.
I ran out of weekly Fable limit near the end of project. I used last couple percents to ask Fable how much would it cost to finish our plan on usage tokens. The answer: \- Publish run, relaxed \7-8m tokens (90-160$) \- Another feature finish \1-1.5m tokens (15-25$) Publish run is divided in sub runs: 1A, 1B, 1C, 1D, 1E, 1F I have burned 212.19$ in 1B 😅 Good estimate Fable. On a serious note tho, who the f\ can afford this? Who will be using Fable? I for sure am quitting my subscription (am on Max 20) - that was not enough with weekly limits, on top of that 250$ tokens lasted me for \2h of plan run.
The amount of tokens it burns is ridiculous. And it does it in a way that is so inefficient it makes me want to throw my pc off the window. Like, why the fuck do you need to use a read tool with the entire context of an entire project 11 times just to get 11 strings out of the same fucking package??? This is extremely dumb and it makes me irrationally angry at how useless it really is for coding. Better yet, why the fuck do you need 3 separate subagents to read miniscule amount of code for 10 minutes straight, burning tokens for mysterious unexplained tasks in the background?? Do you guys unironically do anything coding-related with this piece of shit software? I mean, I was always curious how people manage to burn billions of tokens, but with shit like opencode I guess billions is just the starting point. Legit a tool created purely for the sake of increasing average token consumption
What is the best bang for your buck subscription / AI provider?
This is probably a question as old as time here and in general in any AI talk, but I'm having generally a pretty good time with just the MiMo V2.5 frer using OpenCode Zen (honestly its a lot for $0), but I would like to try some of the better models out there but I can't seem to see which is actually good or cost effective. I don't have that heavy of usage but still every time I find a potentially promising provider I search for reviews on reddit and it either was great then became bad or is bad (Neuralwatt, Llama Cloud and so on) OpenCode Go seems like it should be fine but then again I keep seeing mixed reviews about it and about other providers. i've tried just using OpenRouter paid models which is either works fine, or has some issues with opencode for some reason (Qwen kept failing tool calls) but generally feels like it drains what i deposit pretty quickly. (presuming I use some of the higher performing models, which I guess is kind of given, but I hoped to squeeze more out of it) What would you say is the best bang for your buck subscription / provider currently?
my agents went through 4.3 billion tokens in one day. only 0.3% of that was them actually writing anything
ran a usage report at the end of the day and had to double check the total. 4,294.6M tokens, 3 sessions, one day, one person. the breakdown is where it gets weird: input 2.2M. output 11.4M. cache read 4,192.8M. cache write 88.2M. so 97.6% of everything was cache reads. the agents wrote about 11 million tokens of actual new text all day, which is 0.3% of the total. everything else was them re-reading their own context on every tool call. that's the real shape of agent economics and your bill never shows it to you this way. the number that actually got me: 149.4M of it was human initiated. 4,145.2M was agent continuations. 96.5% of the day happened without me typing anything. friction from the same report: 28 correction loops (an agent went down a wrong path and had to get walked back) 8 context compactions (agent hit its limit, summarized itself, came back missing details) 2 tool failure retries estimated cost at api list prices: $3,091.76. i'm on a subscription so i didn't pay that, but that's what the compute would bill at. blended rate was $0.72 per million because cache reads are cheap. model split: opus did 3,886.3M of the 4,294.6M, about 90%. sonnet handled 129.9M of the small stuff. the gap on long tasks is still very real. what i took away from staring at this: correction loops cost more than tokens. every one of the 28 happened for the same boring reason, the agent was working from stale information about something that had changed elsewhere. compactions are the silent killer. after each one the agent comes back confident and missing half its context. anything not written down somewhere durable is just gone. 9.7 output tokens/sec per stream. parallel streams are the only reason any of this finishes in a day. disclosure so nobody has to ask: the report comes from a tool i'm building (aethereum, the usage command is free, npx aethereum usage). but the numbers are the point, ccusage shows you a lot of the same picture. anyone tracked their human-initiated vs agent-continuation split? curious whether 96.5% is high or normal for people running agents hard.
One small change made our AI agents much more reliable than switching models ever did
x200B; One small change made our AI agents much more reliable than switching models ever did Everyone seems to be chasing the newest model release, but one thing surprised me after spending the last few months working with AI agents. Changing the model rarely fixed the problems we were actually seeing. The bigger improvement came from changing how tasks were structured. Instead of asking an agent to handle an entire workflow in one go, we started breaking everything into smaller, repeatable tasks. Each task had a single responsibility and a clear output. That made it much easier to debug failures, retry specific steps, and understand where things were going wrong. Another lesson was that memory isn't automatically useful. Giving an agent access to everything often made its decisions worse, not better. Carefully deciding what information it should remember turned out to matter more than simply storing more context. I work on agent workflows at Gooseworks, so these are problems I run into pretty regularly, but I'm curious whether others have found similar patterns. For people building production systems: What's been the biggest reliability improvement you've made that had nothing to do with changing the model? How do you decide what an agent should remember versus what should stay external? Have you found a good strategy for keeping agents from getting "context bloated" over time? It feels like we're all reaching the point where architecture is becoming a bigger differentiator than model choice, but I would be interested to hear if others are seeing the same thing.
Building a local AI home lab on an RTX 3090 – looking for advice on what I can realistically run
Hi everyone, I’m in the process of building a local AI home lab and I’d appreciate some advice from people who have already done something similar. Current hardware: ● CPU: AMD Ryzen 9 5900X ● GPU: RTX 3090 (24 GB VRAM) ● RAM: 128 GB DDR4 3600 MHz ● Fast NVMe SSD storage ● Windows (happy to dual boot Linux if it makes a significant difference) My goals are: ● Build and edit ComfyUI workflows locally. ● Generate AI images and videos (LTX 2.3, WAN, FLUX, SDXL, Krea-style workflows, etc.). ● Train LoRAs. ● Run AI agents locally that can code, edit files, search documentation, plan projects, use MCP servers, and automate workflows. ● Access everything remotely while I’m away from home (basically a personal AI workstation). ● Use local models whenever possible and only fall back to cloud models when absolutely necessary. From my own research so far, this is what I believe is realistic: ● Qwen Coder seems to be one of the strongest local coding models for a single 3090. ● Gemma 27B should also run reasonably well. ● Llama 70B appears possible only with heavy quantization and CPU/RAM offloading, so it will be considerably slower. ● 128 GB of system RAM should help with larger models that don’t fit entirely in VRAM. ● Ollama, llama.cpp, LM Studio and vLLM all seem like viable serving options. ● For agents, I’ve been looking at Claude Code, OpenCode, Aider, OpenHands, Roo Code and other MCP-compatible tools. ● I’d also like the agent to browse the web, read GitHub repositories, research Reddit discussions, generate reports, write code, modify ComfyUI workflows and automate repetitive tasks. My long-term goal is essentially to have a “Jarvis”-style AI workstation that I can securely access remotely and use for software development, AI content creation and research. My questions are: 1. Given my hardware, what is the largest and most capable model you’re actually running comfortably? 2. Which coding/reasoning model would you recommend today? 3. Which agent framework has worked best for you? 4. Would you recommend Ollama, llama.cpp, vLLM or something else as the backend? 5. Are there any models that surprised you with how well they performed on a single RTX 3090? 6. If you had my exact hardware, what complete software stack would you build today? 7. Is there anything obvious that I’m overlooking before I invest more time into this setup? I’m looking for real-world experience rather than benchmark numbers. If you’re running something similar on a 3090, I’d love to hear what has worked well (or what hasn’t). Thanks!
The Multi Agent Tax: How to optimize token costs when scaling past 3 agents in production
Hi, If you are running an AI agency or shipping agentic features for your SaaS, you have likely encountered the hidden financial wall of mult agent architectures: The Multi-Agent Token tax. In a sandbox or prototype environment, watching 3 or 4 agents pass messages back and forth in a "group chat" or a "swarm" pattern feels incredible. But the moment you move that exact architecture into a real client environment with thousands of daily users, your API bills compound exponentially. The math gets brutal fast. If Agent A sends a 4,000-token payload to Agent B, and Agent B appends its processing history and passes 6,000 tokens to Agent C, your token overhead runs up to 15 × higher compared to a classic single-turn LLM endpoint. If you do not optimize this, your agency’s profit margins are entirely consumed by the LLM providers. Here are 3 immediate infrastructure changes to stop the bleeding without degrading agent accuracy: 📉 1. Kill Peer to-Peer "Group Chats" (Move to Hierarchical Supervisors) The Mistake: Letting agents openly ping each other in an unstructured mesh network (Agent\A talks to Agent\B talks to Agent\C). This layout triggers redundant context passing and infinite conversational loops. The Production Fix: Enforce a strict Hub-and-Spoke (Supervisor) pattern. Designate a single, lightweight router agent whose sole job is to ingest the initial user state, route the clean request to an isolated specialist agent, and collect the final output. Specialist sub-agents should never talk to each other; they only return payload objects back to the master supervisor, instantly cutting out redundant history bloat. 🧠 2. Implement Short-Term State Ephemerality (The Memory Truncation Pattern) The Mistake: Passing the entire historical log of an agentic loop back into the context window for every new step in a state graph. The Production Fix: Treat short-term agent chat history like an ephemeral caching layer. Use structured data stores to save the exact raw inputs and outputs, but only pass a compressed delta summary into the execution window of the next agent. When using frameworks like LangGraph, use custom node reducers to manually overwrite or pop useless tool logs out of the global state before hitting the next LLM call. 🏷️ 3. Asymmetric Model Routing (Match LLM to Task Complexity) The Mistake: Routing every single sub-agent call in your workflow to premium frontier models like GPT-4o or Claude 3.5 Sonnet. The Production Fix: Audit your agentic graph and break operations into distinct cognitive tiers. Use premium frontier models strictly for the Planning, Parsing, and Routing phases. For predictable tasks like data formatting, simple tool validation, and raw JSON serialization, dynamically route those specific node calls to hyper-fast, low-cost models (such as GPT 4o-mini or Claude 3.5 Haiku) or self-hosted open-source models. 💬 Let's Solve This If you've shipped multi-agent architectures to actual paying clients: What is your current framework setup for routing state (e.g., custom code, Lang Graph, Crew AI)? How are you managing your prompt token budgets to keep your margins intact? Drop your architecture bottlenecks below. Let’s figure it out.
Hey brothers , I'm trying to calculate the monthly token yield on a $60 budget constraint(pro+). Assuming a routing split of 70% Grok 4.5 and 30% Composer 2.5, what kind of token volume can I realistically expect? I'm aiming for around 1 billion tokens per month for my current pipeline. Has anyone run a similar setup, and is that 1B mark mathematically possible within a $60 limit, or would I need to optimize my model selection? Appreciate any insights!"
for the record, i’m running Sol Max on Fast mode so I am burning tokens faster than usual. it does great with backend and the agent usage is amazing. yes it drains my weekly limits however it’s literally impossible to let this model work on its for several hours unintended. it takes the whole 5 hour limit in a single hour to where I have to wait 4 hours. This led to me using a second codex account to continue on the session while I wait for the first account. It also does not help that I cannot even use all of my weekly limit in time before they grant a big limits reset. There’s been so many complains about the current model draining limits faster. Why not just remove the 5 hour limit and let us drain from our weekly usage? I never actually had any problems with the weekly usage, except for the fact that I have been usage reset so many times when I still had a lot of weekly usage left. Sadly now it’s impossible to let Codex run overnight on its own and come back to finished tasks. Since 5.6 has been released I have not drained my weekly usage a single time, but the 5 hour usage drains instantly.
Hey guys, I've been running Qwen 3.6-27b locally on an RTX 3090 for a while now, and it's been genuinely great at solving software issues. However, life happened and I recently had to use Opus 4.8 alongside the Zed editor and the Claude Code agent. While I can definitely see a noticeable jump in pure code quality (Opus is just better in that regard), what really blew me away was the procedure. When assigned a task, the Claude Code agent divides it into actionable steps, always checks the context before acting, makes backups, and runs multiple verifications to ensure the changes didn't break the system. On top of that, it runs additional functionality evaluations, warning you about potential bugs, performance bottlenecks, or race conditions. Honestly, this feels less like a raw model capability and more like an incredibly well-crafted system prompt and agent logic. So, I have two questions for this sub: 1) Prompt Engineering for Agentic Behavior: How can I structure a system prompt (or workflow) to make my local Qwen replicate this kind of deep analytical, methodical, and "safe" behavior? Does anyone have examples of system prompts that enforce this step-by-step verification? 2) Local Coding Agents/Frameworks: What agent or framework do you recommend for complex coding tasks? I currently use Zed and I'm very happy with its raw performance, but it lacks the ability to use sub-agents or plan complex, multi-step tasks. For context, my daily driver is Debian + KDE. Thanks in advance!
RANT Hermes Agent - too bleeding edge - far from production ready
My top issues : \- tool calls not interpreted, displayed as normal text (sometimes it works ) tool\call ... /tool\call \- Session hangs / desync, sometimes the agent interrupt halfway through a session , but I can see the backend LLM being usage still at max \- tools call when interpreted can fail (looking at you camoufox, browser\console etc ...) \- Desktop app has a few really annoying bug \- Hermes update breakage ( they dont care having a stable release) every update breaks something important (last week was compress, then today the desktop app) I can see all the hype, and the potential, but right now I wouldn't recommend hermes for production. Too may issues / interruption wasting time/money/tokens ... right now Hermes is too much of a pain compared to a proprietary tool I tried openrouter's nemotron, ollama 's qwen3.5 qwen3-coder qwen3.6-27b, qwen3.6-35b-a3b .. Some are buggier than others but I never had a smooth session. Am I missing something?
I created a plan with sol ultra, it took about 1.5 hours to create. I then gave the plan back to sol ultra in a new session, that was 28 hours ago. Its been running for 28 hours straight! The plan has 20 tasks that need to get done, so far its finishing up 4/20. Its been using my 5 hour quota and just making it to the next 5 hour quota to keep going lol. I have 45% of my weekly left since the reset yesterday, im on 20x. Ive had to tell it to finish out the current task and do a handover doc and then stop. At this rate it would be going for days and through multiple 20x accounts. I never even set a goal. The results are amazing in comparison to 5.5xhigh but its just so slow, I literally dont have the tokens. Im'a need some help Tibo 😅
I mean, yes its alittle bit "smarter" but is it because its a new model? I feel like its doing editing, generations, commands ect. much much slower. I count results not time spent working. What is wierd is that token use is very rapid compared to gpt 5.5, like its an hourly rate and not what it does. Working for 1h 46m straight while "impressive" on paper when looking at the output and its around 500 lines is strange to me. Excpecially when it used 40% of the weekly limit on xhigh. On ultra it drinks the limit with a double straw and goes 10% weekly per 5 minutes of work. What is going on? Should I just go back to 5.5?
i just installed and setup 9Router and Hermes i did not done anything strange after my first messege of Hello i noticed , Even when i send simple word in new chat like hello and then how are you , it will eat more than 15000+ tokens as input , anyone can help?
The current token consumption is absolutely insane.
Before the update, back when I was using 5.5 Extra High, I could never actually run out of usage. I felt like even with automation tasks running 24/7, it was impossible to hit the limit. But now, with version 5.6 Extra High or the Ultra, it is a different story: 1. With Ultra, I might need a reset after just a single task. 2. Extra High is similar; eg I run two tasks, the credits might run out in less than two hours.. I only have three resets left right now, and it is giving me serious anxiety. I just woke up a few hours ago, and I honestly don't know if I can make it through to the evening.
Original poster: Original publication time: 2026-07-11 18:44:53 UTC Original title: One message used 105k tokens???? Original flair: Question about Claude Code Original URL/media URL: --- Original post body: For some reason when making a site, it not only goes to extremes in the wrong way for researching, keeps rewriting code all the time and also uses tokens manically... What the fk is going on?? This was a simple question that burnt 105k tokens?? Original image(s): Original image 1: Original image 1 Original image 2: Original image 2
Forgive me for the questions, for I have been scrolling, reading, researching, testing, and trial-and-error. I'm looking for options to run a local coding agent. I am currently using Codex with their tiered subscription plans, with extensions that connect to browsers, the local computer, GitHub, etc. I am looking for alternatives to run something very similar locally so that I no longer have to be restricted to the 5-hour usage windows and weekly limitations. I saw some tutorials for Docker and Ollama, but maybe I didn't set them up correctly, or they just don't have the same capabilities I'm looking for. Thanks for any suggestions and walkthroughs!
How do you ensure minimal AI slop while writing code with Cursor in production
Genuine question to folks who are legit software engineers and been using cursor diligently to write production-grade code - what are the best practices you follow? I'm kinda starting now with writing prod code, been using it a lot for prototypes, quick analysis. Currently I just give it existing architecture and design principles related md files to ensure it adheres to existing code. Still, I see it messes up things like the way class or enum or other objects in general (things not explicitly mentioned in the said md files) are written or named or placed. Largely it does great job, though these seemingly small things do matter to me to ensure coding style is consistent. FYI, I'm using opus 4.8.
Hello everybody I launched a fairly comprehensive website audit using three specialized sub-agents: one for design, one for technical issues, and one for legal/compliance matters. I then waited around five hours for the process to complete. Two of the sub-agents apparently reached their usage limits before returning any usable final results. Only the design sub-agent provided a few preliminary observations. The main agent therefore had to redo almost the entire technical and legal analysis itself. The final audit was completed, but the sub-agent delegation provided almost no time-saving benefit and likely increased both the waiting time and resource usage. It also seems that any work performed internally by the two failed sub-agents could not be recovered because they had not sent intermediate findings before reaching their limits. (This is what GPT told me after I asked it if there is anything that blocked him during the process or anything like that) Is this normal behavior for Codex sub-agents? Is there a way to require periodic progress reports, recover partial work, or automatically stop/reassign a sub-agent before it reaches its usage limit? Because this would mean the 10 minutes of work on all the three subagents, and the 5hours usage I had all (or 80% of it) went to trash, because it can't continue where it stopped before the limit usage had been reached ?
Ive been using AI to help me build projects for over a year, and one thing ive always found difficult is having it look over a project and make suggestions on improvement, Not necessarily new features but improving features that already exist without me having to give it what I want improving, Im looking for ways to have AI scan a project and suggest ways of improving current features - I have tried just asking it to scan a project directory and suggest improvements on the current state but It always seems to go off on suggesting new features rather then improving current ones. Curious on how everyone else does it or if people even do that at all and just give improvements from their own use or feedback from others to have improvements made.
you get charged double above 272k, so basically, for about 80k above it, which is 5.6's new limit 353k, you are being charged at 2x the cost. i hope that they will increase the threshold to match the default context with 5.6 by the way, i did not post this under the Complaint flare, but rather Limits, the bot automatically detects and sets it to a complaint mislabeling it, as this is purely informative information about limits. not a complaint. mods should fix the terrible automated detection system for those wondering if this includes subscription-based pricing instead of api, this was the only confirmation i could find, i hope someone at OpenAI can confirm if this is true or not: The current documentation is clearer than what I initially found, but it still stops just short of an explicit sentence saying that the 272K multiplier applies identically to ChatGPT-authenticated Codex usage. Does the 272K multiplier apply to subscription usage? OpenAI now says that Codex usage under ChatGPT plans is calculated in credits per million input, cached-input, and output tokens, and that the credit cost varies with the model, context, reasoning, and tools. The current GPT-5.6 Sol rate is: 125 credits per 1M input tokens 12.5 credits per 1M cached-input tokens 750 credits per 1M output tokens (OpenAI Developers) Those rates map exactly to the API rates using a conversion of 25 credits per dollar: $5 input × 25 = 125 credits $0.50 cached input × 25 = 12.5 credits $30 output × 25 = 750 credits The API documentation then explicitly says that prompts above 272K input tokens cost 2× input and 1.5× output for the full request. (OpenAI Developers) So the evidence is: 1. Subscription-authenticated Codex is now explicitly token-metered. 2. Its credit rates exactly reproduce the API rates. 3. OpenAI says context affects credit consumption. 4. The model’s underlying rate schedule has the 272K multiplier. (OpenAI Developers) However, I still could not find a public OpenAI sentence saying: “For ChatGPT-authenticated Codex, requests above 272K are charged at 250 input credits and 1,125 output credits per million tokens.” Therefore, my conclusion is: It is very likely that the multiplier applies to subscription allowance and purchased-credit consumption, but it is not explicitly confirmed line-for-line in the public Codex rate card. Operationally, I would absolutely assume it applies. Even without the discrete multiplier, reducing repeated 300K-token requests would materially extend your usage allowance anyway.
My AI Agent burned through an API Quota in One Afternoon
I will start with the stupid tax I paid last week. Was testing a recursive agent for a coding workflow using Minimax m3. Left for lunch and came back to a completely drained api quota. It hit a minor json error and got stuck in a useless plan, analyze, retry, and summarize loop. The request count and token burn went vertical. Everyone testing new language models only looks at context windows and single prompt pricing. But in recursive agents, circular looping amplifies the cost of any model exponentially. The root issue was not the base price. My agent just had zero execution boundaries like max recursive depth, single task token caps, rate limits, or an anomaly kill switch. To fix this, I scrapped my hardcoded setup and moved to dynamic routing using an aggregator called Atlas cloud. It puts the models I need behind one endpoint. Now I route cheap long context coding tasks to Minimax m3, heavy agent reasoning and tool use to glm 5.2, and deep debugging workflows to Kimi k2.7 code. Because they sit behind one api layer, I can enforce a global token budget and rate limit. If you build recursive agents, please set a max depth, strict token budgets, retry caps, and timeouts before hitting run. You also need to log every request and model selection locally so it does not infinitely summarize itself in the background.
So how exactly did this use 316.5K tokens on messages??
I cant use claude code, because everytime i try, no matter the prompt, it uses 20\40% of my usage on 300k+ messages context, even though its a completely fresh session. I am super frustrated because i wasn't aware that this bug was happening, and thus alot of what i paid for is just gone in a matter of seconds.
AI can generate a backend in one prompt now. Nobody's really solved who watches it after that.
Every AI builder demo looks the same: describe an app, watch tables/APIs/auth get generated in real time, everyone's impressed. What none of them show is week three. Some specific ways I've seen (or expect to see) this rot in production: Schema drift — the AI adds a column or table mid conversation and the generated API layer doesn't know it exists yet, or vice versa. RLS gaps — a table gets created without row level security, or with a policy that's technically present but a no-op (USING (true) dressed up as a real rule). Works fine in every demo because nobody tests as a second user. Broken auth after a "small" change — a table rename or FK change quietly breaks a login flow that depended on the old shape. API drift — the generated REST contract stops matching the actual schema after a manual tweak, and nothing notices until a client request 500s. Zero monitoring — no one's watching for any of the above unless a human remembers to look. The generation problem is basically solved at this point every major player can turn a sentence into a working schema. The part that's still mostly unsolved is ongoing correctness: something that behaves like an SRE for a backend nobody on the team is qualified to operate. Genuinely curious how people here think about this is "someone/something has to keep watching it" actually true, or is that overkill for 90% of what gets built this way? And if you were designing the "keeps watching it" layer, what would you have it check first?
How are teams handling the silent cost/loop trap with autonomous AI agents in production?
Hey guys, We've been experimenting with building some autonomous ReAct loops for an internal tool. One thing that’s absolutely terrifying to me is the lack of predictable control flow. Last week, we had an agent get stuck in a weird self-correction loop trying to fix a minor tool output error. It didn't throw an exception; it just kept iterating. It ended up burning a massive amount of tokens in a few hours before we caught it manually. It feels like free-form agents are a massive liability for production systems where latency and budgets matter. We are thinking about completely ripping it out and replacing it with a strict, hard-coded state machine graph (like LangGraph) where the LLM can only pick between predefined paths. For those who have actually shipped agentic workflows to real customers: how are you building your guardrails? Are you using strict state machines, or are you successfully running free-form autonomy with token-ceiling safety nets?
Hej, zrobiłem ostatnio kilku agentów którzy pomagają mojemu zespołowi w naszym projekcie i działają świetnie. Management ciśnie teraz żeby uogólnić rozwiązanie żeby wszyscy w firmie mogli go używać. Czy tylko ja uważam że to głupi pomysł i jest ciężki do zrealizowania? Nasi agenci działają świetnie bo są nauczeni jak i co mają robić na projekcie, żeby działali na innym projekcie trzeba ich odkontekstowic co zwiększa pole do potencjalnych błędów i halucynacji bo jak agentowi czegoś nie zdefiniujesz to zrobi to po swojemu czyli często źle przy dużym projekcie gdzie jest dużo zależności wzorców itd. Mając wiedzę i doświadczenie które mam mógłbym pójść do innego zespołu i dorobić im agentów w kilka dni, a zamiast tego marnuje czas na tworzenie rozwiązania dla wszystkich którego prawdopodobnie nikt nie użyje bo będzie robiło dużo błędów o nie będzie się dużo różniło od defaultowego agenta w Claude codę czy innym narzędziu. Czy tylko ja widzę bezsens pracy nad takim rozwiązaniem czy jednak brakuje mi wiedzy na temat rozwiązań AI?
It keeps looping and looking for hyper optimization on already well defined and scoped tasks. The token burn on subscription plan is absolutely insane. I got nothing done in 24 hours, when I was cruising with 5.5xhigh. I give it another 12 hours before switching it back
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Frequently asked questions
Which communities do these leads come from?
Leadverse monitors developer subreddits where AI-assisted coding is discussed daily - from agent workflows and IDE tooling to deployment and security. This page shows posts where developers describe concrete problems or ask for tools.
Why does Reddit work so well for dev tools?
Developers state their pain precisely and publicly, and they actively ask for tool recommendations. A helpful reply from a founder routinely converts - Reddit threads about dev tools also rank on Google and get cited by AI assistants for years.
How do I contact these developers?
This preview hides usernames and links. Leadverse subscribers see the full post and get an AI-drafted, personalized comment or DM for each lead - reviewed and sent by you, so it reads like the genuine recommendation it is.
How current is this page?
It regenerates weekly with the latest matching posts. In the app, new leads land daily, typically within hours of being posted.
Leadverse monitors Reddit for people actively looking for AI coding tools and dev workflow products, filters posts for buying intent, and drafts personalized outreach so you can start the conversation while the lead is still hot.
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