---
type: concept
title: "Model Selection"
description: "Choosing the right LLM per task by trading off quality, speed, and cost, often routing easy requests to cheap models and hard ones to expensive models."
tags: [reasoning, cost, routing, agents, model-selection, benchmarking, reasoning-models, o3]
videos: [i-love-the-karpathy-llm-wiki-but-it-doesnt-scale-heres-what-does, google-just-dropped-a-masterclass-on-agentic-engineering-its-so-good, the-creators-of-claude-code-and-openclaw-dont-prompt-their-agents-anymore, omnigent-the-new-meta-harness-for-every-coding-agent-claude-code-codex-pi-more, googles-agents-cli-the-cli-skills-combination-to-ship-ai-agents-easily, claude-plans-gemini-designs-the-workflow-to-build-beautiful-frontends, harness-engineering-what-separates-top-agentic-engineers-right-now, parallel-claude-code-git-worktrees-this-setup-will-change-how-you-ship, pi-coding-agent-archon-build-any-ai-coding-workflow-no-claude-code-bloat, im-building-an-ai-dark-factory-that-ships-its-own-code-public-experiment, the-next-evolution-of-ai-coding-is-harnesses-heres-how-to-build-them, coding-agent-reliability-explodes-when-they-argue-new-adversarial-dev-technique, youre-hardly-using-what-claude-code-has-to-offer-its-insane, the-subagent-era-is-officially-here-learn-this-now, turn-claude-code-into-your-full-engineering-team-with-subagents, the-way-we-use-ai-will-completely-change-in-2026-hot-takes, is-gemini-3-really-the-best-ai-ever, github-is-the-future-of-ai-coding-heres-why, learn-90-of-building-ai-agents-in-30-minutes, claude-sonnet-45-the-new-coding-king-sonnet-45-vs-gpt-5-codex, i-built-my-claude-code-subagents-dream-team-to-create-any-ai-agent, my-top-20-lessons-from-building-100s-of-ai-agents-super-actionable, context-engineering-101-the-simple-strategy-to-100x-ai-coding, the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here, the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide, build-a-chatgpt-style-app-for-your-n8n-ai-agents-in-minutes, brand-new-stealth-llm-released-on-openrouter-its-a-beast, introducing-archon-an-ai-agent-that-builds-ai-agents, this-is-hands-down-the-best-way-to-build-ai-agents, lets-settle-the-o3-mini-vs-r1-debate-for-coding-once-and-for-all, you-have-to-try-agentic-rag-with-deepseek-r1-insane-results, deepseek-r1-just-revolutionized-ai-forever, pydantic-ai-deepseek-v3-the-best-ai-agent-combo, how-to-dominate-with-ai-in-2025, revealing-my-complete-ai-agent-blueprint, boltdiy-the-future-of-open-source-ai-coding-whats-next, weve-been-building-ai-agents-wrong-until-now, our-boltnew-forks-most-requested-feature-is-finally-here, im-addicted-to-windsurf-send-help, 10-n8n-tips-in-10-minutes-to-10x-your-ai-automations, ottodev-is-becoming-the-best-open-source-ai-coding-assistant, qwen-just-casually-started-the-local-ai-revolution, how-to-use-boltnew-for-free-with-local-llms-and-no-rate-limits, ollama-openais-swarm-easily-run-ai-agents-locally, stop-paying-for-gpt-claude-other-llms-get-them-all-in-one-place, im-building-the-best-open-source-ai-coding-assistant-with-your-help, i-forked-boltnew-and-made-it-way-better, deploy-any-ai-app-to-the-cloud-fast, llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent, local-vs-cloud-llmsrag-lets-finally-end-this-debate, v0-claude-a-game-changer-for-building-ai-apps, complete-guide-to-langserve-make-your-ai-agent-actually-useful, i-found-the-limits-of-the-most-popular-llms, youre-wasting-time-coding-your-ai-agents, the-best-tool-for-ai-agent-workflows-langgraph-full-guide, ai-is-too-expensive-if-you-dont-do-this, ai-agents-the-future-of-ai-or-a-passing-fad, new-llama-3-model-beats-gpt-and-claude-with-function-calling, function-calling-with-any-llm-for-local-ai-agents-feat-langchain-huggingface-and-llama-3, i-made-a-custom-ai-chatbot-site-in-47-lines-of-code, the-only-ai-developer-roadmap-you-need-in-2024]
created: 2026-07-21
updated: 2026-07-21
---

# Model Selection

Model selection is matching each task to the cheapest model that can still do it well, instead of defaulting to the most powerful LLM everywhere. The discipline reduces to one line: "just make sure you're using the right model for the right task" [0:12:41]. The reason it pays off is economic. Running one top-tier model for everything means "you have to pick a model that is good enough to handle that 5% or 10% of complicated requests even if the model is really expensive" [0:00:45], yet "you don't have to use just one large language model and you can save a ton of money by using more than one" [0:01:02].

## How it works

The dominant pattern is **routing**: a cheap first-pass model classifies request difficulty, then hands off. "You take a request and you have a first step which is a large language model that decides the complexity of the request" [0:01:32], so "if it can be handled by a simpler model you can spend less money by going and routing that to the cheaper model like llama 3... so you're saving dollars upon dollars but your output is just as good" [0:08:16]. The same logic drives per-step assignment inside agent workflows and [subagents](./subagents-pattern.md): "the work that we're delegating to the subagents are the kind of things that are super token heavy, but also don't require as much reasoning power. So, it's overkill to use something like GPT 5.4 high or claude opus 4.6" [0:04:39], and in [Archon](../entities/tools/archon.md) "every single node in this archon workflow, I can actually decide what model am I going to use" [0:11:43]. A canonical split is "Claude Code for the implementation... Codex for the review and then for all of our context loading and exploration up front... a smaller model like Kimi K 2.7" [0:11:57].

Selection is also getting less absolute in the [harness](./ai-coding-harness.md) era. Google's masterclass showed a team "take a model from outside the top 30 into the top five just by creating an AI layer of rules and workflows" [0:14:27], and Cole notes "you can make sonnet work as well as Opus if you have the right system" [0:14:41]. The corollary is to lean on effort levels and cheaper models more: "now we're leaning more into the system we build for AI coding versus always relying on the most reasoning power possible" [0:17:06].

## Watch-outs

Swapping models is riskier than it looks. "Swapping large language models can actually be pretty dangerous even if you are swapping to an LLM that is supposed to be better in just every way" [0:30:10], because "different llms need different prompts" [0:04:00]. The practical advice for beginners is to not over-optimize up front: "don't actually worry about picking the perfect LLM up front, especially when you're using a platform like OpenRouter" [0:09:22]. And picking a tool is itself a model choice: "you're picking the harness when you choose the tool" [0:02:14].

## Contrasts with

- [Reasoning Models](./reasoning-models.md) - a specific selection axis: reasoning models buy accuracy at the cost of latency and tokens, so route hard/logical tasks to them and keep the fast conversational path on a lighter model.
- [Reasoning Model as a Tool](./reasoning-model-as-a-tool.md) - Architecture where a fast, lightweight model drives the conversation and calls a slow, powerful reasoning model as just another tool, keeping the flow nimble while still getting reasoning-grade answers on the hard step.

## Related

- [Provider Independence](./provider-independence.md) - the freedom to select any model presumes you have not locked into one vendor.
- [Training Cutoff](./training-cutoff.md) - an AI model's knowledge is frozen at the date its training data ends, so it silently knows nothing about events, libraries, or versions released afterward.
- [Local & Self-Hosted AI](./local-ai.md) - the local branch of selection, chosen for privacy and cost ("if you want a local model for privacy reasons... Mistral 3.1 Smaller Qwen 3 are the ones that I recommend" [0:09:46]).
- [AI Coding Harness](./ai-coding-harness.md) - the system that can make a cheaper model perform like a pricier one.
- [Subagents Pattern](./subagents-pattern.md), [Agent Teams](./agent-teams.md) - where per-role model assignment lives.
- [Agent Evaluation](./agent-evaluation.md), [Agent Observability](./agent-observability.md) - how you actually verify a model swap helped rather than hurt.
- [Deterministic Workflows](./deterministic-workflows.md), [Agentic Coding](./agentic-coding.md), [Agentic RAG](./agentic-rag.md), [Tool Calling](./tool-use.md) - contexts where the right-model-per-step rule is applied.
- [Mixture of Experts](./mixture-of-experts.md) - A model architecture that routes each token through a subset of specialized expert networks, contrasted with a dense Transformer, explaining why two similar-looking reasoning LLMs behave very differently.
- [Model-Specific Prompts](./model-specific-prompts.md) - Different LLMs need different prompts, so a serious system keeps a library of model-tuned system prompts instead of reusing one prompt written for a single frontier model.
- [AI Tech Stack](./ai-tech-stack.md) - Your chosen collection of tools and services (LLM, framework, database, automation, hosting) combined into one working AI system, picked to fit and kept simple and reusable.
- [LLM Cost Optimization](./llm-cost-optimization.md) - Systematically cutting the bill for running LLM applications at scale by sending the bulk of cheap-to-serve traffic to far cheaper models and paying premium rates only for the rare hard requests.

## Tools

- [OpenRouter](../entities/tools/openrouter.md) - one API across providers so you can swap models freely and cheaply.
- [Archon](../entities/tools/archon.md) - assigns a different model to each workflow node.
- [Claude](../entities/tools/claude.md), [Codex](../entities/tools/codex.md), [GPT-4](../entities/tools/gpt-4.md), [Google Gemini 2.0 Flash](../entities/tools/gemini.md) - the frontier models Cole benchmarks and mixes.
- [DeepSeek R1](../entities/tools/deepseek-r1.md), [Llama](../entities/tools/llama.md), [Qwen](../entities/tools/qwen.md) - the cheaper and local options routing sends easy work to.
- [GPT-4.1 Nano](../entities/tools/gpt-4-1-nano.md) - OpenAI's smallest, cheapest model in the GPT-4.1 line, used for the per-chunk context-generation call in contextual retrieval because the task needs almost no reasoning power.
- [GPT-5.4 Mini & Nano](../entities/tools/gpt-5-4-mini-nano.md) - OpenAI's small/fast model releases marketed explicitly for subagents and AI coding; nano reportedly beats Claude Haiku 4.5 at a fifth the price and ~188 tok/s, anchoring the "subagent era" thesis.
- [GPT-5 Codex](../entities/tools/gpt-5-codex.md) - OpenAI's coding-specialized GPT-5 model, the head-to-head opponent Cole benchmarks against Sonnet 4.5 on an identical real-world Stripe integration task.
- [Claude Opus 4.8](../entities/tools/claude-opus-4-8.md) - Anthropic's reasoning-heavy Claude release that owns planning, page copy, integrations, and fixes in Cole's mixed-provider frontend workflow.
- [Claude Sonnet 4.5](../entities/tools/claude-sonnet-4-5.md) - Anthropic's coding-focused model release that topped the coding and agentic-tool-use benchmarks over Opus 4.1 and shipped as the default model in Claude Code 2.0.
- [Code Llama](../entities/tools/code-llama.md) - Meta's open-source code-specialized LLM family whose larger 34B variant fails builds that the smaller Qwen 2.5 Coder 32B handles, showing capability beats parameter count.
- [Berkeley Function Calling Leaderboard](../entities/tools/berkeley-function-calling-leaderboard.md) - A public benchmark that ranks LLMs on function-calling accuracy using tasks meant to represent real agent and enterprise workflows.
- [Claude 3.5 Sonnet](../entities/tools/claude-3-5-sonnet.md) - Anthropic's mid-2024 flagship model and the only LLM Cole could not break in his agentic stress test, making it his clear winner for multi-step tool-using workflows.
- [OpenAI o3-mini](../entities/tools/o3-mini.md) - OpenAI's small reasoning model, used as Archon's reasoner to write the scope document before the coder agent builds, and the head-to-head rival to DeepSeek R1 for coding.
- [Kimi K2](../entities/tools/kimi-k2.md) - Moonshot's Kimi model (K2.7), used as a cheap driver and exploration model in Cole's harness via a Kimi subscription to control per-token cost.
- [MiniMax M2.7](../entities/tools/minimax-m2.md) - A cheap, fast, capable LLM that Cole routes Claude Code to (via environment variables) to drive the entire dark factory economically at high throughput instead of Anthropic models.
- [Abacus.AI](../entities/organizations/abacus-ai.md) - The AI company behind ChatLLM, offering a consolidated multi-model chat product with connectors to Teams, Jira, Confluence, Google Drive, and Slack.

## Sources

- [AI is TOO EXPENSIVE if You Don't Do This](../sources/ai-is-too-expensive-if-you-dont-do-this.md) - "[0:01:02] you don't have to use just one large language model and you can save a ton of money by using more than one"
- [AI Agents: The Future of AI or a Passing Fad?](../sources/ai-agents-the-future-of-ai-or-a-passing-fad.md) - "[0:08:16] if it can be handled by a simpler model you can spend less money by going and routing that to the cheaper model like llama 3... so you're saving dollars upon dollars but your output is just as good"
- [The Creators of Claude Code and OpenClaw don't Prompt Their Agents Anymore?!](../sources/the-creators-of-claude-code-and-openclaw-dont-prompt-their-agents-anymore.md) - "[0:12:14] That's part of the problem of why it's so expensive... you don't always need to spend the most per token for every step of your workflow."
- [The Subagent Era Is Officially Here - Learn this Now](../sources/the-subagent-era-is-officially-here-learn-this-now.md) - "[0:04:39] the work that we're delegating to the subagents are the kind of things that are super token heavy, but also don't require as much reasoning power. So, it's overkill to use something like GPT 5.4 high or claude opus 4.6."
- [The Next Evolution of AI Coding Is Harnesses - Here's How to Build Them](../sources/the-next-evolution-of-ai-coding-is-harnesses-heres-how-to-build-them.md) - "[0:21:01] certain nodes, like classification, they don't need a lot of reasoning power. So, we can make it a lot more token efficient, a lot cheaper by just using Haiku for our model."
- [Google Just Dropped a Masterclass on Agentic Engineering (It's SO Good)](../sources/google-just-dropped-a-masterclass-on-agentic-engineering-its-so-good.md) - "[0:14:41] LangChain was able to increase it 13.7 points. Like that's the difference between Sonnet and Opus... you can make sonnet work as well as Opus if you have the right system"
- [Coding Agent Reliability EXPLODES When They Argue (New Adversarial Dev Technique)](../sources/coding-agent-reliability-explodes-when-they-argue-new-adversarial-dev-technique.md) - "[0:17:06] now we're leaning more into the system we build for AI coding versus always relying on the most reasoning power possible with these bigger large language models."
- [My Top 20 Lessons from Building 100s of AI Agents (Super Actionable)](../sources/my-top-20-lessons-from-building-100s-of-ai-agents-super-actionable.md) - "[0:30:10] swapping large language models can actually be pretty dangerous even if you are swapping to an LLM that is supposed to be better in just every way."
- [Learn 90% of Building AI Agents in 30 Minutes](../sources/learn-90-of-building-ai-agents-in-30-minutes.md) - "[0:09:22] don't actually worry about picking the perfect LLM up front, especially when you're using a platform like OpenRouter"
- [Harness Engineering: What Separates Top Agentic Engineers Right Now](../sources/harness-engineering-what-separates-top-agentic-engineers-right-now.md) - "[0:02:14] this might not feel like harness engineering because you're not defining anything, but you're picking the harness when you choose the tool."
- [Claude Plans, Gemini Designs: The Workflow to Build BEAUTIFUL Frontends](../sources/claude-plans-gemini-designs-the-workflow-to-build-beautiful-frontends.md) - "[0:12:41] So just make sure you're using the right model for the right task."
- [The Way We Use AI Will Completely Change in 2026 (Hot Takes)](../sources/the-way-we-use-ai-will-completely-change-in-2026-hot-takes.md) - "[0:03:02] We're going to have that specialization more and more where it depends what we're building that will determine the LLM that we use specifically."
- [You're Hardly Using What Claude Code Has to Offer, it's Insane](../sources/youre-hardly-using-what-claude-code-has-to-offer-its-insane.md) - "[0:13:03] medium is the default... you can go between low, medium, and high. And then opus specifically also has max effort which is going to use a lot of tokens but oftentimes this is worth it when you get really stuck on a problem."
- [This is Hands Down the BEST Way to Build AI Agents](../sources/this-is-hands-down-the-best-way-to-build-ai-agents.md) - "[0:19:46] we are using a reasoning llm at the start of the workflow something like DeepSeek R1 or OpenAI 03 mini and this guy is going to take the requirements from the user"
- [The Ultimate Guide to Local AI and AI Agents (The Future is Here)](../sources/the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here.md) - "[0:18:00] 32 billion when you start to get into this range is when I'm actually genuinely impressed. I'm like, Oh, this is actually pretty close to the performance of some of the best cloud AI"
- [Claude Sonnet 4.5 - The New Coding King? (Sonnet 4.5 vs. GPT 5 Codex)](../sources/claude-sonnet-45-the-new-coding-king-sonnet-45-vs-gpt-5-codex.md) - "[0:00:00] I know that benchmarks don't tell us the entire story, but right now Sonnet 4.5 is at the top of the charts, even beating out previous anthropic models like Opus 4.1"
- [bolt.diy: The Future of Open Source AI Coding - What's Next?](../sources/boltdiy-the-future-of-open-source-ai-coding-whats-next.md) - "[0:04:00] it has become very very obvious as we have been working on bolt. DIY together that different llms need different prompts"
- [I Love the Karpathy LLM Wiki but it Doesn't Scale. Here's What Does.](../sources/i-love-the-karpathy-llm-wiki-but-it-doesnt-scale-heres-what-does.md) - "[0:06:35] I still love using Pydantic AI for all of my production agents because coding agent SDKs, like the Claude agent SDK or Codex SDK... they're slow because they're made for longer agentic coding tasks, and they're also more token heavy."
- [Google's Agents CLI: The CLI + Skills Combination to Ship AI Agents EASILY](../sources/googles-agents-cli-the-cli-skills-combination-to-ship-ai-agents-easily.md) - "[0:08:36] for anything you're running yourself like your second brain, where you care more about the output quality over the speed and the cost, these are fantastic."
- [Omnigent: The New Meta-Harness for EVERY Coding Agent - Claude Code, Codex, Pi, More](../sources/omnigent-the-new-meta-harness-for-every-coding-agent-claude-code-codex-pi-more.md) - "[0:07:13] a lot of people feel like Codex is best at reviewing and Claude is best at implementing."
- [Parallel Claude Code + Git Worktrees: This Setup Will Change How You Ship](../sources/parallel-claude-code-git-worktrees-this-setup-will-change-how-you-ship.md) - "[0:21:38] You aren't stuck always using the highest capability model. You can go to something lower like Haiku or Sonnet... when you're doing something that you want to be cheap and fast"
- [Let's Settle the o3-mini Vs. R1 Debate for Coding ONCE and for ALL](../sources/lets-settle-the-o3-mini-vs-r1-debate-for-coding-once-and-for-all.md) - "[0:01:40] it certainly is going to sway my decision for which llm I use to code and that's one of the biggest things that I do with AI"
- [The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)](../sources/the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md) - "[0:13:40] I am using GPT 4.1 Nano. So, I'm using the smallest model offered from OpenAI. It is dirt cheap and we don't need that much reasoning power here"
- [Pi Coding Agent + Archon: Build ANY AI Coding Workflow (No Claude Code Bloat)](../sources/pi-coding-agent-archon-build-any-ai-coding-workflow-no-claude-code-bloat.md) - "[0:12:00] We're overriding this at the individual node level. And then we're using OpenAI Codex with GPT 5.4 Mini."
- [I'm Building an AI Dark Factory That Ships Its Own Code (Public Experiment)](../sources/im-building-an-ai-dark-factory-that-ships-its-own-code-public-experiment.md) - "[0:17:01] Archon allows us to specify different models for different workflows and run things with code instead of coding agents"
- [Turn Claude Code into Your Full Engineering Team with Subagents](../sources/turn-claude-code-into-your-full-engineering-team-with-subagents.md) - "[0:07:35] You can even change the model that each of our subagents are using... let's just use haiku for the model."
- [I Built My Claude Code Subagents DREAM TEAM to Create Any AI Agent](../sources/i-built-my-claude-code-subagents-dream-team-to-create-any-ai-agent.md) - "[0:08:08] If there is a certain part of your development workflow handled by a subagent that doesn't need as much power in the LLM, you can save tokens by using Haiku, for example."
- [Learn 90% of Building AI Agents in 30 Minutes](../sources/learn-90-of-building-ai-agents-in-30-minutes.md) - "[0:09:46] if you want a local model for privacy reasons... then Mistral 3.1 Smaller Qwen 3 are the ones that I recommend"
- [STOP Paying for GPT, Claude, & Other LLMs - Get them ALL in One Place](../sources/stop-paying-for-gpt-claude-other-llms-get-them-all-in-one-place.md) - "[0:00:48] I just have to go and try my shot with another llm that I think could probably handle this specific problem better"
- [Is Gemini 3 Really the Best AI Ever?](../sources/is-gemini-3-really-the-best-ai-ever.md) - "[0:00:52] how do we actually know if an LLM is really the next big thing?"
- [Context Engineering 101 - The Simple Strategy to 100x AI Coding](../sources/context-engineering-101-the-simple-strategy-to-100x-ai-coding.md)
- [GitHub is the Future of AI Coding (Here's Why)](../sources/github-is-the-future-of-ai-coding-heres-why.md)
- [Build a ChatGPT Style App for Your n8n AI Agents in MINUTES](../sources/build-a-chatgpt-style-app-for-your-n8n-ai-agents-in-minutes.md)
- [BRAND NEW "Stealth" LLM Released on OpenRouter (It's a BEAST)](../sources/brand-new-stealth-llm-released-on-openrouter-its-a-beast.md)
- [Introducing Archon - an AI Agent that BUILDS AI Agents](../sources/introducing-archon-an-ai-agent-that-builds-ai-agents.md)
- [You HAVE to Try Agentic RAG with DeepSeek R1 (Insane Results)](../sources/you-have-to-try-agentic-rag-with-deepseek-r1-insane-results.md)
- [DeepSeek R1 Just Revolutionized AI Forever](../sources/deepseek-r1-just-revolutionized-ai-forever.md)
- [Pydantic AI + DeepSeek V3 - The BEST AI Agent Combo](../sources/pydantic-ai-deepseek-v3-the-best-ai-agent-combo.md)
- [How to DOMINATE with AI in 2025](../sources/how-to-dominate-with-ai-in-2025.md)
- [Revealing my COMPLETE AI Agent Blueprint](../sources/revealing-my-complete-ai-agent-blueprint.md)
- [We've Been Building AI Agents WRONG Until Now](../sources/weve-been-building-ai-agents-wrong-until-now.md)
- [Our Bolt.new Fork's Most Requested Feature is FINALLY Here](../sources/our-boltnew-forks-most-requested-feature-is-finally-here.md)
- [I'm Addicted to Windsurf, Send Help](../sources/im-addicted-to-windsurf-send-help.md)
- [10 n8n Tips in 10 Minutes to 10x Your AI Automations](../sources/10-n8n-tips-in-10-minutes-to-10x-your-ai-automations.md)
- [oTToDev is Becoming the BEST Open Source AI Coding Assistant](../sources/ottodev-is-becoming-the-best-open-source-ai-coding-assistant.md)
- [Qwen Just Casually Started the Local AI Revolution](../sources/qwen-just-casually-started-the-local-ai-revolution.md)
- [How to Use Bolt.new for FREE with Local LLMs (And NO Rate Limits)](../sources/how-to-use-boltnew-for-free-with-local-llms-and-no-rate-limits.md)
- [Ollama + OpenAI's Swarm - EASILY Run AI Agents Locally](../sources/ollama-openais-swarm-easily-run-ai-agents-locally.md)
- [I'm Building the BEST Open Source AI Coding Assistant with YOUR Help](../sources/im-building-the-best-open-source-ai-coding-assistant-with-your-help.md)
- [I Forked Bolt.new and Made it WAY Better](../sources/i-forked-boltnew-and-made-it-way-better.md)
- [Deploy ANY AI App to the Cloud FAST](../sources/deploy-any-ai-app-to-the-cloud-fast.md)
- [Llama 3.2 is INSANE - But Does it Beat GPT as an AI Agent?](../sources/llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent.md)
- [Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate](../sources/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md)
- [V0 + Claude: A GAME CHANGER for Building AI Apps](../sources/v0-claude-a-game-changer-for-building-ai-apps.md)
- [COMPLETE Guide to LangServe - Make Your AI Agent Actually Useful](../sources/complete-guide-to-langserve-make-your-ai-agent-actually-useful.md)
- [I Found the Limits of the Most Popular LLMs](../sources/i-found-the-limits-of-the-most-popular-llms.md)
- [You're WASTING TIME Coding Your AI Agents](../sources/youre-wasting-time-coding-your-ai-agents.md)
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