Diego Pacheco Tech blog

AI everywhere, agents everywhere. We just finished the first quarter of 2026, and a lot happened in those first 3 months. It feels like 3 years have passed, not three months. Opus 4.6 really changed the game, but software engineering and distributed systems are not solved by agents; we saw hype at the top of the hype, with huge,  unrealistic expectations that need to be dialed back and properly adapted. The less you know about AI and Agents, the more impressed you are, and no, you cannot get rid of all engineers. AI Alone does nothing and cannot self-verify to the point that systems of systems can be completely automated and run hands-off. Maybe we will get there one day, but we are not there, and no matter what people say, no one can predict when this will happen. Could be 30 years or even more. Karparthy already said that: Zero to Demo is easy . Demos are not impressive anymore. Zero to production is still a very different story. Multi-Agent Systems: Process as Agents Pad...

People building agents today are mostly doing one-shot. Meaning they write one and that's it. Yesterday, I was watching the YC Lightcone podcast: "Inside Claude Code With Its Creator Boris Cherny" and one of the things Boris, creator of Claude Code and head of Claude Code in anthropic, said is that they delete the CLAUD.MD a lot because they want the new models to take over. That insight tells us a lot that we cannot just settle for whatever prompts we have. Besides that, depending on how we write the prompt, we might use more or fewer tokens; there are ways to better structure agents, workflows, and skills. For this blog post, I will cover some lessons learned while building and improving agents, workflows, and skills. I did a bunch of experiments; in fact, I wrote 7 incarnations of my agent skill. To test the agent's skill, I asked the agent to build a Twitter-like application so I could evaluate the quality of the code and solution as a proxy for the agent's s...

AI coding Agents like Claude Code , OpenAI Codex , and Gemini CLI have disrupted how software engineering is done. IMHO, the most disruptive agents are Claude code and Codex. However, a lot of things already happened, some progress has been made, and there is some evolution in the space. We saw the birth of custom and subagents to avoid passing the whole context window down, custom commands  to have more control over a workflow, or when a specific task is executed. Hooks  add more determinism and make sure tests and linters are executed as part of the guardrails. From the explosion of MCPs to Multi-Agent Systems. There are many interesting changes and evolutions happened, we learned somethings while some things are still to be learned. For this blog post, I will cover some of the evolution in AI coding agents (mainly around Claude code). I did a lot of POC with agents, 74 Agent-related POCs at the moment. One thing I keep saying is that POCs are getting expensive, now not ...

The One does not simply use AI Agents in production. Before using AI agents in production, we need to understand that LLMs are token prediction machines and by nature are non-deterministic . No matter how good you specs are, AI will drop packages and make mistakes. Lack of determinism is just one aspect we need to keep in mind. We also need to keep in mind that it's very easy to jailbreak the models . Adding a chatbot directly to customers has dangers and not only in a security sense, but also for misuse and potentially legal problems. Even if that is all somehow managed and risk is minimized with proper guarantees, one still does not just use agents in production. 20-15 years ago, we would not just deploy APIs to production; we would use an API Gateway. Considering agents and LLMs, we need the same: an AI gateway infrastructure. What happens if your API provider (Anthropic, Google, or OpenAI, for instance) is down? Is your business down? 

PMI has a whole discipline about risk management. The DevOps movement has several principles to reduce operational risk, such as continuous deployment, infrastructure-as-code, progressive rollout patterns, traffic splitting, and more. Financial institutions might terminate or restrict business relationships with clients and even categories of clients to eliminate risk, hence derisking. Risk management was very popular in the 90s and even 2000s, but it's not dead, IMHO. Nobody talks about risks, nobody even monitors risks. I don't know why that happened or even if it's true for other industries besides technology.  The DevOps movement teaches us many things, one of which is post-mortem or blameless incident reviews. However, incident reviews are great practices if done right, meaning having engineers on the call and actually driving lessons learned for real.  Besides that, the problem is that we are having after the fact, which only prevents feature problems if we do our hom...

Many people think that today, Gen AI is the revolution of the machines. That AI will make an engineer 3-5x more productive. Well, that’s not true and not even close to being true. Let’s pretend it’s true for one moment, then we can apply some scale economics to Generative AI, especially with AI coding agents like Claude Code or OpenAI Codex.   Let me be honest: I don’t think that using AI you can get 3-5x the productivity from engineers right now. There are so many things I need to debunk here; it’s even hard to choose one to start with. But for a moment, just. For the sake of argument, let’s believe it's true(just for a moment). That will help you to understand why AI has suddenly become so appealing to companies.

AI Agents and vibing are growing in popularity. It's time for us to think about what use cases make sense to use them and what use cases it would be a mistake to use them. AI is changing how people work, learn, and behave. Should I vibe or not vibe? Vibing can be useful when AI Agents have limits. Vibe coding has several drawbacks, including security challenges, hallucinations, and incorrect facts. However, there are a couple of use cases where vibing can be useful, when: It's not a priority : Imagine there is code that you would never write, tools you would never create, simply because you have bigger priorities and would never spend weeks or months on such tasks. It's not your specialty : Let's say you would never learn C# to make a Windows extension of your application, but with AI Agents or even with vibing, you can get that done without much effort. Limited Resources : You either lack the time or the funds to undertake such a task. AI agents/vibing can allow you to...

July 2024: I wrote the first blog post about The Dark Side of LLMs . During these 7 months, many things have changed; the usage of AI and LLMs in engineering has kept growing. LLMs and AI are cool. However, they are not a free lunch and have consequences. If you have not read the first blog post of this series, go there and read it because it will be relevant to this one. One significant open-source development in LLM was  DeepSeek .DeepSeek is interesting for two factors: one that is considerably cheaper and second that is open source. Deepseek introduced a series of optimizations like parallelism, chain of thought (COT), which step by step so we can fix the model where is wrong and usage of Reinforcement Learning (RL) along side with Destilation. RL is how robots roll and self-driving cars also move in a city.  Deepseek's being open source is great for the community; however, we also see interesting moves from big tech companies like  Meta ,  Microsoft ,  Goog...