Affordable LLM Solutions for Coding Automation

What happens when you vibe code with Claude 4.5 for an hour? You accidentally solve a $1M problem 🎯 Started playing around with an idea, and 60 minutes later had a fully functional PyPI package that turns expensive LLM API calls into free, self-hosted models. LLM Intercept is a proxy server that captures your LLM interactions and automatically formats them for fine-tuning smaller models. Think of it as your training data pipeline on autopilot. The workflow is beautifully simple: 1️⃣ Install via pip: pip install llm-intercept 2️⃣ Route your OpenAI-compatible API calls through the proxy 3️⃣ Automatically log and format all interactions in SQLite 4️⃣ Export clean datasets in Parquet or JSONL format 5️⃣ Fine-tune compact models like Liquid AI's LFM2 series (350M-2.6B params) 6️⃣ Deploy your custom model locally at zero marginal cost Key capabilities that make this compelling for model developers: ✅ Universal compatibility with OpenRouter, llama.cpp, and any OpenAI-compatible endpoint ✅ Full streaming support with SSE ✅ Function calling and tools support ✅ Web dashboard for monitoring and analysis ✅ Simple Password-protected admin interface ✅ Smart export with system prompts stripped Perfect for developers using permissive models like DeepSeek-V3.2 (MIT), Qwen3-235B (Apache 2.0), or GLM-4.5 (MIT) who want to build their own specialized models. Transform your expensive API dependencies into efficient, self-hosted solutions. The future of AI is small, specialized, and running on your own infrastructure. 🔗 GitHub: https://lnkd.in/g4whHmAu 📦 PyPI: pip install llm-intercept

I was spending $600+ running ~25 automations on Claude. This was too much for simple automations. Now it costs me ~11/mo. Here’s what actually fixed it: 1. Most automations don’t need an LLM at runtime: Stripe alerts, Slack updates, usage reports, PR digests… These are just cron + API calls + basic logic. If the output is predictable, don’t run a model. No tokens. No latency. No weird failures. 2. If you do need an LLM, you’re probably overpaying: A lot of tasks are classify, extract or lightly transform. They don’t need Sonnet/Opus-level intelligence. But most agent tools (Claude, OpenAI and wrappers on top of these) don’t expose real model control. So you end up running everything on expensive models by default. That’s the trap. Switching to Hermes agents let me actually pick models. Gemini Flash is ~40x cheaper than Sonnet. For these tasks, it performs the same. This alone cut a huge chunk of the cost. 3. Use frontier models to build, not to run: I didn’t hand-write any of this code. AI wrote the scripts, set up the flows, and got everything running in a few hours. Pre-AI, this would’ve taken days → which is why tools like Zapier made sense. Now the better pattern is: Use a strong model once to generate the system. Then run that system cheaply forever. Everything now runs on: $7/mo Amazon Lightsail instance ~$4/mo in LLM usage Same automations. ~95% cheaper. -- P.S. If you're looking to automate presentation workflows then try out Alai (YC W24)'s MCP.

Wasting Inferences: Auto-Fixing Bugs with an LLM Swarm (for < 10¢!) Steve Yegge's "Revenge of the Junior Developer" predicts Agent Clusters/Fleets emerging around 2026. But what if we could experiment with that core idea today? This week on "Works on My Machine," I explore automating bug fixes directly from Asana using multiple LLMs simultaneously via Aider. The demo shows a workflow where assigning an Asana task triggers a Sublayer agent. This agent then scripts the Aider coding agent, instructing it to fix the reported bug using GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash – generating three separate PRs for the same task. Key Takeaways: ➡️ Agent Clusters Now: While not deeply coordinated, basic agent workflows tackling coding tasks are feasible today. ➡️ Cheap Redundancy: "Wasting inferences" across these 3 powerful LLMs cost less than $0.10 for this bug fix! Makes parallel attempts viable. ➡️ Compare & Contrast: Automatically get multiple potential fixes and compare different implementation approaches generated by different models. ➡️ True Automation: Move beyond manually prompting agents via chat; trigger complex coding workflows directly from project management tools like Asana. Watch the full demo video showing the setup and results, and read the breakdown (including specific costs & code links) on this week's Substack post (link in comments).

I've replaced 200+ lines of LLM parsing code with 3 FREE open-source tools. All free. 33K+ GitHub stars combined. Most developers know LangChain and Ollama. These flew under the radar. 1️⃣ Instructor (8K+ ⭐) https://lnkd.in/ek-eX4E7 Forces LLMs to return structured data. No parsing. No regex. No "please format as JSON." Define a Pydantic model. Instructor guarantees the LLM fills it exactly. OpenAI, Anthropic, Gemini, local models. Before: 200+ lines of try/except validation spaghetti. After: 3 lines. Type-safe. Every time. 2️⃣ Outlines (10K+ ⭐) https://lnkd.in/evY8aXpN Takes structured generation further. Constrains the model at the token level. It literally cannot produce invalid output. Want a JSON object with exactly 5 fields? It samples only tokens that keep the schema valid. Before: "Please return valid JSON" + pray. After: Guaranteed schema compliance. Works with any open-weight model. Hugging Face, llama.cpp, vLLM. 3️⃣ LiteLLM (15K+ ⭐) One interface for 100+ LLM providers. OpenAI, Anthropic, Cohere, Bedrock, Azure, local models. Same API for all of them. Swap providers in one line. Add fallbacks. Track costs across vendors. Before: Rewrite your entire codebase when switching models. After: Change one string. Why these matter: The gap between "LLM demo" and "production system" is mostly plumbing. Structured outputs. Provider abstraction. Reliability. These three tools close that gap without building everything from scratch. All open-source. All actively maintained. All production-ready. Which tool are you adding to your stack first? 👇 ♻️ Repost for someone still writing regex to parse LLM outputs.

Let's build an AI coding assistant ⬇️ Let me show you how to build a coding assistant using LLMs that runs entirely on your computer, using open-source technologies. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Coding assistants like Github Copilot or Cursor are great. However, they have one small problem: As you use them, your source code and any data embedded in it is constantly sent to the LLM provider (OpenAI, Microsoft, Anthropic…) Which means that your data becomes THEIR training data ⚠️ This might not seem like a big deal when you work on a side project. However, most companies are 0 interested in sharing their data with others, especially in the Healthcare, Insurance or Banking industry. So, as a developer working in most companies out there, you need to find a way to leverage the power of LLMs for fast development, without sharing your data with the LLM provider. The question is, how to do that? 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 The building blocks: > CLI interface, in this case we use Typer to build the CLI > Ollama to run LLMs locally. In this case we will use qwen2.5-coder, one of the best LLMs for code generation. Feel free to use any other LLM you want from the Ollama library. > A very simple Python program (aka an agent) that "sees" all the code you have in your repository and uses it to answer questions and provide code modifications. This is the core of the solution, and one of the areas where you can improve it, and get as fancy as you want. 𝗙𝗮𝗻𝗰𝘆 𝗹𝗶𝗸𝗲 𝘄𝗵𝗮𝘁? The agent we have built here takes a very pragmatic approach to the problem. It's a single LLM that embeds a stringified version of your code in its prompt. This solution works as long as your codebase is not too large, and can fit inside the LLM context length. If you need to work with larger codebases, you can use a chunking and searching strategy (aka RAG) to only include the most relevant code in the prompt. Moreover, if you want to make the agent more powerful, you can add tools that can help the agent not only suggest code modifications, but also run them. 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗶𝘀 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁? You first need to install > Ollama on your machine. > uv to run the CLI in a sandboxed environment. Once installed, you can run the CLI with the following command: $ uv run python cli.py \ --source-code-path <PATH_TO_YOUR_REPO> \ --agent-type single-llm \ --llm-provider ollama \ --llm-model qwen2.5-coder:7b-instruct where `<PATH_TO_YOUR_REPO>` is the path to source code you are developing and you need help with. I am using qwen2.5-coder:7b-instruct, but you can use any other model you want from the Ollama library. Be aware that the larger (and potentially better) the model you use, the longer it will take to generate a response. 𝗚𝗶𝘁𝗵𝘂𝗯 𝗿𝗲𝗽𝗼 On the comments below you will find the link to the Github repository I created ⬇️⬇️⬇️ ---- Hi there! It's Pau 👋 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 on LinkedIn and for more high-signal ML content!