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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Looking for the best open-source alternatives to OpenCode
Hey everyone, I'm looking for recommendations for frontends/apps that act as a workspace for AI coding agents. I was using open code but i know notice too much bugs, the main one being, blank replies when the context expand
Now that Codex x20 was nerfed, I'm a bit troubled, what to sub for. Unfortunately, I use a lot of tokens, but most of my programming tasks are not super complicated. I was thinking about sharing it with Anthropic (two 5x plans), so I get access to the best frontend models while still having cheap Sol access. I'm still a bit puzzled though, because I remember they lowered their allowance as well. Do any of you have multiple subs? What are the best options nowadays? Any experiences with other providers?
Curious how people here are using AI on their Nimo these days. Do you mostly stick with cloud tools like ChatGPT or Claude, or have you started running things locally? What matters most to you right now? Privacy, speed, convenience, or something else? Also curious if anyone has run into limitations or pleasant surprises when trying local AI on their machine. Drop your honest take below. Different opinions are welcome.
I’m trying to compare these harnesses: \- Claude Code \- OpenCode \- Codex \- DeepSeek Harness (DSH) \- Pi Harness \- Bionic LM Studio \- and any other good ones I’m mainly interested in coding quality, agentic work, token/cost efficiency, accuracy, speed, context management, MCP/plugins, local/weaker model support, resource usage, and ease of use. Has anyone actually tested multiple harnesses with the same model + same task + same settings and compared the results? If you know of any benchmark, article, video, GitHub repo, or person who has done a fair apples-to-apples comparison, please share it. And if you’ve personally used several of these, which one do you prefer and why?
Boa tarde, pessoal! Estou desenvolvendo uma plataforma para presença digital e, até pouco tempo atrás, meu fluxo de trabalho principal era com Claude Code e Cursor. Porém, o Cursor mudou o modelo de cobrança. Antes tínhamos 500 requisições rápidas por US$ 20 e, quando acabavam, dava para continuar usando em menor velocidade. Agora, o limite é estritamente financeiro (os mesmos US$ 20), e meus créditos não duraram nem metade do mês usando o modo auto. Gostaria de saber quais alternativas vocês estão usando. No momento, eu tenho à disposição: ChatGPT Plus (teste grátis) Gemini Pro (ganhei 18 meses) GitHub Copilot Pro (plano Student) O Claude é excelente, mas o limite de tokens esgota rápido quando o ritmo de código aperta. O Gemini e o Copilot têm janelas de contexto limitadas e, para lógica mais complexa, achei que deixam a desejar. Gostei muito do Codex, mas também tenho receio de esbarrar na limitação de tokens para projetos grandes. Cheguei a ver modelos chineses (Alibaba, Bytedance...). Vocês recomendam alguma IA que tenha uma cota de tokens mais generosa e que entregue um código bom se fornecermos um planejamento de arquitetura bem detalhado? Ou a melhor estratégia ainda é ter paciência com os limites e seguir revezando entre os modelos do Claude e Codex?
Suggestions and recommendations for local Ai for programing
Hi! I'm kinda new to this and would like to get some info from other peoples experiences What I'm looking for is a setup for programming, mostly to do it along side me but code reviewing and such wouldn't be bad addition At the moment, i got 2 3090s with 24gb each for a total of 48 (worth noting that not headless at the moment), and 128gb of ram (dd4) I did look into the 3090 github, with qwen 3.8 27b in mind but id love to read what people experiences and what you use, which models, harnesses and whatever else thanks for whoever decides to comment
Solo builders: which AI tool holds your actual context, and why that one?
I used to keep a running "project brain" doc. Two thousand lines of markdown: decisions I'd made, things I'd tried and killed, stuff that was half done and waiting. Then I switched coding tools and the whole thing might as well not have existed. Re-briefing the new one took most of an afternoon, and for the next two days I'd remember some detail mid-task and just groan. After that I stopped trying new tools. Not because the one I use is good, honestly it fumbles things every week. But it holds all the accumulated context, and that alone is worth more to me than whatever the new tool does better. I stay because leaving is too expensive. Saying that out loud feels gross. So which tool ended up being home base for you, and what made it that one? Have you ever stayed with a worse tool because moving the context wasn't worth it? If you did switch, what did you actually do, paste a brief, hand over a summary file, start from zero? Uncomfortable question I keep circling: what if something handled all of this for you, in the cloud over MCP, so every agent and tool got the same context, decisions, failed attempts, conversations, skills, procedures, tasks, and you never touched a file again? Would you pay for something that fixes this? Have you ever thought about paying for a tool to manage all of this for you, or has the thought never crossed your mind? Also, weird tangent, does any of your setup live on your phone? I do a lot of thinking out loud into mine and the context always ends up stranded on my laptop. Is that just me? Out of curiosity, what are you building with all this, your own product or something else?
Newbie question, can you run a local agent for coding comparible to Codex?
Been doing some research about local LLMs, I have a high end gaming pc that I was thinking to convert into an AI setup to help with code for a project of mine. I've been using Codex over the last year but thinking of switching to a local agent as it seems more cost-efficient. From what i've seen it definitely won't be as powerful as Codex but wondering if it's even worth doing. These are my specs: AMD R7 RTX9800X3D RTX5080 GPU 16gb 64gb RAM
Qual o melhor/plano IA para usar e não ficar na mão?
Pessoal boa noite! Estou com um grande dilema aqui, qual IA assinar rsrs. Para ter um contexto, eu tenho uma conta pro do Gemini que até agora serve para implementar coisas pequenas. E para coisas grandes estava utilizando o modelo do Qwen 3.8max pois ele estava sendo um modelo de ponta, segundo o frontEnd Arena ele ficava somente atrás dos modelos de ponta da openAi e Anthropic, e seu preço e limite pareciam muito válidos para mim. Porém o harnees dele eh horrível, então estou querendo mudar minha assinatura e estou muito na dúvida, realmente tenho usado bastante IA no dia a dia então preciso de qualidade e quantidade. E agora que o modelo do Qwen mudou o limite para mensal chegou a hora de pagar pelo codex ou Claude code. Quero saber de vcs, qual seria o melhor? Não tô nem falando de qual IA coda melhor pq isso é outra discussão, mas basicamente qual eu posso contratar, a pergunta básica é: qual tem o maior limite de tokens? E se o plano pro deles da pra trabalhar tranquilo na semana ou hoje em dia somente o plano plus de 500 reais que garante isso?
This previous week I ran into a weekly limit having to wait 2 days for the reset and I today after my reset I am nearly 60% after only an afternoon and 2 days-ish. The first month after the first Max 5x plan I was not even near the weekly limit usage, not I consume it with just 1 session? I am currently running Matt Pococks setup, wayfinder, to-spec, tickets, implement. Everything and right now I am running mostly implement but I am running out. What are you guys using for agentic coding? I don't know if it is just that Matt Pocock skills are consuming a lot of Opus 5.5. In Opus 5 I was running even 5 agents doing different implement tasks, would reach up to 20% and still have room for the 80%.
[Help] What is the Best Context Extending App or Plugin you Recommend?
I like to use Deepseek Harness as my vibe coding harness. It's great and support local models. The issue is that most models I can run locally have context size of about 262K. Therefore, for long coding sessions, I need a memory management tool. DSH comes with a context compaction tool that I can run manually. The issue is that compaction starts to fail after a few rounds. So, looking at DHS market place, I came across this plugin called Billion Context ( The claim is I can use have long sessions. The issue is that it's a heavy context compression skill that keeps nudging the LLM to compact every few turns, which takes 5-10 minutes of work, significantly extending a normal coding session. Worse, after God knows how many rounds, the LLM seems to spend most of its time unpacking the compressed context, which fills its working context, which leads the model to compress again the text. This ended up with the LLM looping. So, what plugins do you use with DSH or your favorite harness? What tips or tricks could you share? I am aware I can use sub-agent to work on a specific task and return a summary to the orchestrator. That helps, but I still need to manage the context window for the main agent too. If it's not clear by now, memory is the one area I think resources must go to by they don't. I don't think context compaction is the solution. I hate it with every fiber in my body.
MarkTechPost put out a roundup of local and open source agent harnesses. A few stood out to me, along with a couple I came across separately. OpenCode: Supports Ollama, LM Studio, llama.cpp and 75+ providers, so you have a lot of flexibility around the backend. Goose: Linux Foundation project, written in Rust, with 70+ MCP extensions. Probably one of the projects with the most institutional backing right now. Aider: Uses plain text diffs instead of function calling. The approach feels a bit old, but it still works really well when you care about clean commits and Git history Cline: VS Code native with Plan and Act modes plus per action approval. Good setup if you want to see exactly what a local model is doing before it makes a change. Tutti: Open source Apache 2.0 build focused on running multiple agents locally. Useful when you want to keep agent state and changes in one place. OpenHands: More container focused and needs a heavier setup, especially if you’re running larger local models. Better suited to sandboxed runs. Codex CLI: Apache 2.0 with Ollama and LM Studio support, plus sandboxing on Linux and Windows instead of giving the model unrestricted access. what are you guys using . is there something i'm missing out on ? lmk
Is there any provider offering truly unlimited tokens for open-source LLMs at a flat monthly price?
I'm looking for an inference provider that offers unlimited tokens (no caps, no rate-limit gotchas) for open-source models on a flat monthly subscription. Models I'm interested in: • Qwen 3.8 27B • GLM 5.3 Flash • DeepSeek V4 Flash • Other open-weight LLMs I don't want pay-per-token pricing. I'd rather pay one fixed monthly fee and use the models as much as I need. Does anyone know a provider like this, or is there any plan that comes close? Real-world experience with speed, reliability, and fair-use limits would be really helpful too. Thanks in advance!
What’s is the best system to run locally on Mac still learning myself
What’s is the best system to run locally (hosted) on Mac still learning myself, can I run tasks locally if I have work to do with clients or want to multi task is it better than using a ai platform like gpt or Claude?
Please let Cloud sessions & Local sessions communicate with each other!
I have a cloud session inside my Claude Code app and a local sessions. Both sessions are inside my Claude Code app, but can't communicate with each other, which is a bummer. I would love to have a local orchestration Agent, who sents agents working in the cloud and can help out with anything, that the cloud agent is limited with. Any workaround?
I've been curious about trying a local setup for vibe coding and keeping everything on my own machine. I mostly work on small projects and like the idea of having the code and development environment right there without much extra setup. For anyone doing this, what does your workflow look like and what have you learned along the way?
I've been coding for a while and want to bring AI into the mix without paying for a bunch of different tools. I'd mainly use it for debugging, writing code and getting through larger projects. I have a decent PC too so running something locally is an option. What's been working well for you?
Looking for a free AI coding tool like Google Antigravity.
Looking for a free AI coding tool like Google Antigravity. Completely free Unlimited tokens/usage Can write and edit code Can work with multiple files Can act as an AI coding agent No daily/monthly limits Open-source or local tools are also okay Any suggestions?
Looking for a free AI coding tool like Google Antigravity.
&x200B; \- Completely free \- Unlimited tokens/usage \- Can write and edit code \- Can work with multiple files \- Can act as an AI coding agent \- No daily/monthly limits \- Open-source or local tools are also okay Any suggestions?
How practical is it to run AI locally for real applications?
I’ve been looking into the idea of running AI on your own machine or infrastructure instead of sending every request to a cloud API. It seems especially interesting when an application needs to work with private documents or sensitive information. For developers who have experimented with local AI, how has it worked for you in practice? Do you see local AI becoming a realistic option for more applications, or are cloud-based models still much easier to work with for most use cases?
Anyone else disappointed in how Grok bots handle @cursor\ai cloud agents? It seemed like the development was running into all sorts of failures and regression. I decided to layer CodeX on top of Cursor as a senior dev/quality reviewer and that stack seems to work better. I even got it to run for almost 24 hours. I'm assuming its because OpenAI's best models are better than Grok 4.7, which is my default on Cursor. I like the idea of pairing a powerful, independent check on a cheaper workhorse. What stacks have you found helpful?
Hello experts! Can a Mac Studio with 64gb run a decent llm for agentic coding? I’m currently using codex with OpenAI and Deepseek, open code with Longcat 2.5 (free) looking for something that can do simple to medium tasks . Any help / guidance appreciated.
[Workflow] Requested Workflow: Maintaining Project Context Across Claude Code Sessions/Accounts
Requested Workflow: Maintaining Project Context Across Claude Code Sessions/Accounts Workflow value: 85/100 Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: intermediate Categories: Quality Control, Context & Memory, Debugging, CLAUDE.md Original source: r/ClaudeCode post/comment What problem this solves Loss of project context and continuity when switching between different Claude Code sessions or user accounts on the same codebase, leading to inefficient re-explanation and potential errors. Summary A user is seeking a structured workflow, including specific prompts and context storage strategies (e.g., CLAUDE.md), to effectively hand off project context between different Claude Code sessions or user accounts. The goal is to ensure the incoming session has all necessary information (decisions, constraints, unfinished tasks) without manual re-explanation. Why it is useful This Reddit item is valuable because it clearly articulates a critical and common pain point in LLM-assisted development: the challenge of maintaining project context and continuity across different sessions or user accounts. A well-defined workflow addressing this problem would significantly enhance developer efficiency, reduce redundant effort, and minimize errors on larger projects. The request is specific enough to guide the creation of a highly reusable and impactful workflow. Workflow 1. Prompt the outgoing Claude Code session to generate a comprehensive project handoff summary. 2. Store the generated context in a persistent and easily accessible format (e.g., CLAUDE.md, a dedicated handoff document, or multiple files). 3. Instruct the incoming Claude Code session to read, verify, and integrate the stored context before resuming work. 4. Implement a strategy for incrementally updating and managing the project context to prevent important details from being lost as the project evolves. Tools / artifacts - Claude Code - CLAUDE.md file - Custom handoff prompts/templates - Separate handoff document/files Validation signals - User explicitly states current manual summaries 'still left gaps'. - User is looking for 'reliable handoff workflow'. - User requests 'actual prompts or templates that worked for you'. Limitations - The post itself is a request for a workflow, not a complete workflow solution. The actual prompts and detailed steps are missing. Rate this workflow Upvote this post if the workflow is useful, reproducible, or worth recommending. Downvote if it is vague, outdated, unsafe, overhyped, or not reproducible. Reply if it worked for you, failed, is outdated, or has a better alternative. --- This post was generated automatically from the workflow library database.
Has anyone here experimented with building a more complete self-hosted AI setup rather than just running a local LLM?
I’ve been looking into this because I want to understand how practical it is to keep AI workloads, documents, and private data on my own infrastructure instead of sending everything to hosted services. Running a model locally is one part of it, but once you start adding things like RAG, document processing, knowledge bases, and AI agents, the setup can become quite a bit more involved. I’m still trying to understand what a good self-hosted setup looks like in practice, especially when the goal is to work with personal or private documents without relying heavily on external APIs. For those who have been running self-hosted AI for a while, how have you approached this? Did you build everything from separate tools, or have you found a platform that brings most of these pieces together without making the setup unnecessarily complicated?
When is it worth it to buy your own hardware in order to run AI agents locally?
As far as I understood it, it only makes sense if you care about \- absolute data privacy, \- uninterrupted operational resilience, or \- high-volume continuous execution that makes cloud API costs or rate limits unsustainable Other than that it does not make sense to spend like 1k (excluding electricity cost) to run AI agents locally. Because cloud subscription costs like 10-20€ a month. It would take me like 5-10 years to break even. But the hardware probably gets old before that already. With cloud solution I can always choose the latest models. So, unless the subscription fee is low, it doesn't make sense to buy hardware (RTX, raspberry pi AI hat, Mac mini, etc.) for the sole purpose to run AI agents. Am I right or did I miss anything?
I want Two AntiGravity IDE Running on a Single Machine and Working on the Same Repo But they must Acknowledge Each Other Like Their Code Editing and Generation Settings must be Synchronized and Stuffs an Gemini 3.8 Flash is too good for its Speed and the Precision and Now the Limit is that I could do Better If I can Like Have the MultiAgents working on that Single Code Repo and What are the Possibilities ?? and I do not Want that CLI bullshit or the Standalone AntiGravity thing and Like What are the Best Possible thing that I can Have that Kind of AgenticSwarm related Stuffs !! and I do Thankful to the Google and AntiGravity that have the people more Accessible to those Agentic Stuffs and yes Please !! Let me see the things that are Similar Like the AgneticSwarm things that the Google related products could offer please !! TQ so much !! Like Two Different Google AI Acc, one on IDE and one on Standalone App on same Host Machine and Working on Same Repo, on same Prompt and same MD File but those Agents do not have the Connection and the Synchronization or the Collaboration or am I missing something that are already there some Kind of SwarmLike thing and I intentionally avoid that CLI usage because I Feel Like I do not the Proper Control or the Visibility !! and I do not even Like that Standalone AntiGravity App at all!! I only Want the IDE thing with total control but with more Agents at Least Two Agent !! Because 5 Hr Reset limit usage is Like only 1.5 Hours last !! and I cannot Afford the Other tools Like the Claude or the Codex or Whatever !!! and I do Hope that We can pull up that with AntiGravity !! and I also Hope that Even if the Gemini not getting us a new Pro they should expand the usage limit !!
i was browsing yesterday when i discovered this RSIAgent where it claims to have Sel improvement, and i was amazed by the description. When we hear that an AI is learning most of us imagine the model itself getting retrained more data, more training, more computing power, and eventually a smarter model. But what if the model doesn't actually need to change? There’s a growing line of research around AI systems that can explore/remember/test what they discover/ and reuse that knowledge later. One example I found interesting is RSIAgent. Instead of relying on a single AI to figure everything out, the system uses multiple agents with different roles: exploring an environment, attempting tasks, and checking whether the discoveries actually work. The interesting part is that useful discoveries can become reusable knowledge. So the next time the AI encounters something similar, it doesn't necessarily have to start from zero. That got me thinking: Maybe “AI that improves itself” won't look like a model constantly rewriting its own brain. Maybe it'll look more like a junior developer who keeps a notebook. It tries something. It fails. It figures out why. It writes down what worked. And the next time the problem comes up, it already knows where to start. We're still pretty far from the sci-fi version of self-improving AI. But the direction is interesting. The future might not just be about making models smarter. It might be about building systems that make better use of what the model already knows. What do you think is this a more important direction for AI than simply making models bigger?
I keep running out of usage to support my vibe coding hobby with OpenAI. My Mac is very powerful with a lot of ram. Can I use any local models for swift app design ?
How can we mathematically reduce memory/context loss in AI agents?
I'm trying to understand and research a problem with AI assistants and AI agents. One problem I notice is that when I have a long conversation with an AI, it sometimes loses track of earlier questions, decisions, or information. After some time, it feels like the AI starts from the beginning or asks me questions that I already answered. For example: 1. I ask a question. 2. We discuss it for a long time. 3. I provide important information and decisions. 4. After many more messages, I ask something related to the earlier discussion. 5. The AI cannot properly connect the new question with the previous context. I understand that LLMs have context-window limitations, and there are approaches such as summarization, retrieval, vector databases, and different types of memory. But I want to approach this as a research/problem-solving question, rather than simply adding more storage. My main question: Can we use mathematical or statistical methods to decide what information should be remembered, what should be compressed, and what should be retrieved when needed? For example, I'm thinking about something like: Memory importance score = f(relevance, frequency, recency, relationship to current question, past usefulness) Then the system could: Keep highly important information in working memory. Compress less important information. Store older information in long-term memory. Retrieve only the information relevant to the current question. Continuously update the importance score based on future conversations. I'm interested in researching whether this could be modeled using things like: Probability/statistics Information theory Entropy Bayesian methods Embeddings and similarity Attention mechanisms Reinforcement learning Graph-based memory Retrieval algorithms Optimization methods What I want to figure out Before building the system, I want to understand: 1. What mathematical formulation would make sense for this problem? 2. How can we measure whether a memory is actually useful? 3. How can we mathematically decide when to keep, compress, discard, or retrieve information? 4. How can we measure memory quality over a long conversation? 5. Are there existing research papers or algorithms that already address this problem? I'm especially interested in research-oriented answers. If you have worked on LLM memory, context management, RAG, agent memory, information retrieval, or long-context models, I'd really appreciate your thoughts. I don't want to simply increase the context window. I'm interested in whether we can design a more efficient memory-selection and retrieval system using mathematical/statistical methods. What mathematical approach would you start with?
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