Thoughtworks - Technology Radar 34

Intro

Twice a year, Thoughtworks publishes its Technology Radar, an opinionated guide to the current technology landscape. It organizes notable technologies and practices (which they call blips) into four quadrants: Techniques, Tools, Platforms, and Languages & Frameworks; and four rings that express how confident they are in recommending each one, from "Adopt" down to what used to be called "Hold". I have been following the Radar for several years now, and reading each new edition has become something of a ritual for me. Volume 34 came out in April of 2026, but this time I was only able to read it in July, so parts of it already felt slightly outdated... which says as much about the current pace of the industry as it does about my reading backlog. Here are my thoughts on this edition.

General Comments

The first thing that caught my attention was a change to the Radar itself: the "Hold" ring has been renamed to "Caution". I dislike this change. It feels like an unnecessary cave in to the current hype machine, which insists that everything new must be flawless and that pointing out problems is bad for business. Being able to take a stand and argue against certain things, when one has strong arguments, is important for any serious conversation about technology, and softening the language of the ring that exists precisely for that purpose seems like a step in the wrong direction.

The distribution of blips across the rings tells an interesting story. There are 62 blips on Assess and 30 on Trial, against only 17 on Adopt and 9 on Caution. That overwhelming concentration in the middle rings feels very symptomatic of the times we are living in: there is an explosion of new things appearing, and it is necessary to at least keep track of most of them, while investing time to go deep into only a few. It is also striking how few blips landed on Caution - eight of the nine are Techniques, one is a Tool, and there are none at all in Platforms or Languages & Frameworks. The Platforms quadrant, in fact, has zero blips in both Adopt and Caution: everything in it sits in the middle, still being evaluated, with nothing yet considered either safe enough to fully recommend or problematic enough to warn against.

All four themes of this edition are related to AI: "The challenge of evaluating technology in an agentic world", "Retaining principles, relinquishing patterns", "Securing permission-hungry agents" and "Putting coding agents on a leash". It is the second time that this is the case, in previous editions there used to always be at least one theme dedicated to something else, as an effort to keep a broader view of the ecosystem. The four themes seem to form two couples: the first two are about how we humans are changing to adapt to this new model of working, while the last two are about the technical side of embedding generative AI models within a useful "exoskeleton" that is both effective and safe.

Highlights

OpenClaw is the only tool placed under Caution, and to me it is a clear portrait of the current state of AI hype. It made a huge impact when it was first released, and just a few weeks later it was already far less talked about... by now, it is rarely mentioned at all. I was always skeptical about the "hyper-personal AI assistant" category itself, as I see little value in delegating relevant choices of my life to an artificial intelligence. But beyond my personal preferences, the trajectory of OpenClaw illustrates the broader pattern: there is a huge push towards adopting every new thing that appears, without any clear success story that has proved itself over sufficient circumstances.

The agentic coding tools, on the other hand, are where the consolidation is most visible. Cursor reached Adopt, and it was one of the first agentic IDEs I tried, early on. I had an overall good experience with it, though not a very remarkable one. It holds the distinction of being the only agentic coding tool with which I hit a token budget limit while coding (the only other time that happened was with Claude Code, and that was because I was testing Fable, a model with a more restrictive budget). Since the limit appeared unexpectedly, midway through development, I switched to Windsurf and never went back: overall, I preferred working with Windsurf. On Assess, Pi is one I find very promising: I have seen some very interesting proof of concept projects using the Pi SDK as a framework on top of which entire TypeScript systems are built, UI and backend included. It seems like a great candidate to bridge the gap between AI agents and traditional applications, enabling truly agentic applications, and I am very interested in testing it soon. Also on Assess is OpenCode, which became a true milestone in my routine - it is now my tool of choice for simpler or more exploratory tasks, and I wrote a dedicated post about it recently. And finally, Claude Code reached Adopt, having indeed become the industry standard for agentic AI coding: I have been using it extensively, reserving it for the complex tasks that demand state-of-the-art intelligence.

Small language models appearing on Assess caught my attention for a different reason. By "small", the Radar means models of 3B parameters and below, which sits just outside the range I most commonly explore in my personal open source projects - JenAI, LLP and Chargen are built to work with models in the 8B to 70B range. Adding the frontier models I use for development, there is now a whole spectrum of model sizes, each serving its own purpose. I like to think this shows we are starting to reach some level of maturity in employing AI for actual use cases, moving beyond mere experimentation: choosing the right size of model for the job is the kind of engineering decision that only becomes relevant once the technology is being put to real work.

Curated shared instructions for software teams reaching Adopt was particularly satisfying to see. As an InnerSource champion, I have been talking within my company about how AGENTS.md and similar instruction guidelines for agents are now an important and integral part of any project, and how they enable more effective collaboration even from outside contributors. I have given a series of talks about this in internal events already, and I plan to at least write a blog post on the topic soon.

Codebase cognitive debt, placed under Caution, names one of my main concerns with the current shift to AI-assisted software engineering. I have developed software across the whole spectrum that goes from full vibe-coding (just ask the AI and go with whatever it produces, without even looking at the code) to purely human-written code, and I have had successful results at every point of it. But the characteristics of those successes are different, and being distant from the code still does not feel as reliable and sustainable as being hands-on. A big part of being proficient at developing a software system lies in the mental map one has of it, and not being actively engaged in writing the code erodes that map very quickly. This fundamentally changes the process of software engineering. When humans write all of the code, the more you work on a system, the better you become at working on it, because your mental map keeps expanding. When humans stop looking at the code, the relationship inverts: the more you work on the system, the less proficient you become, as the code evolves in ways you can no longer clearly picture. There is no turning back, and the current push in the industry is for humans to be as far out of the loop as possible in order to gain development speed. But we have not lived with this model long enough to have clear success stories that have stood the test of time and proved it sustainable... and until we have those stories, I remain cautiously skeptical.

Things Tested And To Test

Beyond the highlights, several other blips crossed paths with my own explorations. Among the things I have already tested are LangGraph (which I have used as my default choice of AI agents framework - the Radar moved it out of Adopt and into Trial, as there are newer alternatives that can compete with it now), Google's Agent Development Kit (ADK) (which I used briefly when assessing different agent frameworks, and enjoyed more than other alternatives I also tried such as CrewAI, as it maps better to a software engineering mindset - the Radar's main criticism is that it is still pre-GA) and HTML Tools (the first time I see this explicitly named, but an approach I have been following for a while: whenever I ask a coding agent for a report, I ask for the findings consolidated in a nicely stylized standalone HTML page, which reads much better than the default markdown).

On the list of things I want to test are Mastra (a TypeScript-native open source framework for building AI applications and agents), team of coding agents (a technique I have only limited experience with so far), code intelligence as agentic tooling (I have not yet explored much of the Language Server Protocol as a way for LLMs to work more efficiently, but it is high on my list) and mutation testing (an idea I have known for years and was always skeptical about for human-written code, but as a "test-the-test" approach for AI-generated test suites, which realistically no one will fully review, it finally starts making sense to me).

Closing Thoughts

Stepping back, the picture this edition paints matches what I see in my daily work. Kent Beck describes the life of a product in three phases, his "3X" model: Explore, when you cheaply try many things in search of what works; Expand, when something has caught on and you scale it while removing bottlenecks; and Extract, when the ground is stable enough to optimize for efficiency. The AI-dominated landscape of this Radar feels like an industry moving from Explore into Expand: the explosion of new things being created continues (hence the crowded Assess and Trial rings), consolidation is beginning around a few winners (hence Claude Code and Cursor on Adopt), but there is still no concrete success story that has stood the test of time... which is precisely why so much of the Radar remains in the middle rings, and why I keep some healthy skepticism about the most aggressive promises being made.

The next edition of the Technology Radar should be released around October or November of this year. I intend to once again read it (hopefully closer to the release date this time) and share my thoughts here on the blog.

Exploring opencode

I started using opencode around May of 2026, and it truly felt like a milestone in incorporating AI into my daily routine. I mentioned in my May monthly recap that I intended to write a dedicated post about the experience, this is that post.

Some context first. I started using AI coding agents (or harnesses, as I prefer to call them) around August of 2025, with Cline on VSCode, and then moved on to Claude Code in January of 2026. In both cases this happened at work, for purely professional purposes, coding and other software engineering tasks. It was only when I began experimenting with these tools on my personal computer, for a much broader range of tasks, that I eventually discovered opencode. It was a spark that changed a lot of how I think about and use harnesses, for a few reasons.

What Sets It Apart

Install And Start Using

The first thing that struck me is how simple the setup is. With every other harness I have used or looked into (Cline, Claude Code, Codex, etc.), there is a whole ritual before you can do anything: create an account, choose a subscription plan, log in through the tool, and so on. With opencode, you install it and start using it. That is the entire onboarding. It sounds like a small thing, but it completely changes the feeling of picking up the tool for the first time.

Free Access To Reasonably Good Models

The second reason is that opencode currently offers free access to reasonably good models, without any severe limits. The obvious caveat: it is impossible to know for how long this will be sustainable, and opencode might well start limiting usage or requiring a paid subscription in the future. The slightly less obvious caveat: every session needs to be treated as likely shared with the companies that created the model, as the simplest benefit a company can get from giving access to their models for free is data for future training... so, best not to share any personal or sensitive information. Regardless, right now it makes opencode a perfect tool for experimentation.

By lowering the cost of exploration to basically zero, it allows everyone to try the harness for all sorts of things, not just coding. These tools have access to terminal commands, beyond reading files and accessing the web - which means that anything that can be done on a computer can be explored through AI with them. And a huge amount of our society runs on computers, so the potential is enormous. To give a few examples of things I have done: searching for a public, free web radio station from any country (Argentine rock forever!) and playing it directly through the terminal, looking up lyrics and other info about new music I found, and debugging and fixing a broken game installation by letting the agent look at the actual configuration files. Without the burden of knowing you have a certain limit of tokens to spend, you feel free to explore all sorts of random things that an AI agent might do with a computer.

I am even considering making opencode the very first thing I install, the next time I setup a brand new OS installation (likely Linux, as it is my default OS). I find it intriguing to think what an AI-based distro would look like, though I would not want to go to the extreme yet. In my mind just being able to bootstrap the OS with opencode, and then using it to install everything else that I need (as it would have access to the package manager of the distro) by just talking with the computer feels really cool.

Open And Provider-Agnostic

The third reason is that opencode is a fully detached, open source agentic harness that can work with models from several providers. Most tools in this category fail at least one of these criteria: they are IDE-first (Cline), closed source, or tied to the provider that created them (Claude Code with Anthropic, Codex with OpenAI). Pi is an alternative that is also detached, open source and provider-agnostic, but it is far less complete when it comes to features and capabilities.

Being provider-agnostic also means opencode can be used with local models. So even if the free access to hosted models disappears in the future, it will still be possible to run it for free with local ones. That is a kind of insurance that no closed, single-provider tool can offer.

Limitations

Despite all the positive aspects, there are some limitations as well:

Model Quality

While the free hosted models are good, they do not reach the same level of quality as frontier models. For more complex or nuanced tasks, they often do not achieve the best results. This can be worked around with more guidance and handholding from the user, but it takes more work. For the absolute best quality, proprietary models (which usually work better with their own company's harness) are still the way to go.

Context Management

Context management is often a problem. Most of the free models offered have a context size of at most 128k tokens, which gets consumed very quickly - the tool seems to be quite aggressive about how much it puts into the context. It does perform automatic summarization when it gets close to filling the available context, which is good, but more often than not the summarization loses some information that is essential to the task at hand. In practice, this means the quality of the results degrades very quickly, making opencode not very suitable for long-running tasks.

Closing Thoughts

For me, the real significance of opencode is not any single feature, but what the combination enables: a tool that anyone can install and immediately start using to explore what AI agents can do with a computer, at no cost. The frontier of quality still belongs to the proprietary models and their harnesses, and the limitations around context are real. But as a gateway for experimentation, and as an open, provider-agnostic foundation that will keep working regardless of what any single company decides, it has earned a permanent place in my toolbox. I expect to keep exploring what these agents can do well beyond coding, and I am sure some of those experiments will end up here on the blog.

Monthly Recap - 2026-06 June

After two very intense months of working overtime in April and May, June finally saw things gradually getting back to a more sustainable pace. I chose to take it easy and not use all of the newly available free time for personal work though, as I felt like I was very close to getting burnt out if I maintained the same rhythm. I hope that taking a few weeks now, mid way through the year, to rest and re-energize will allow me to have a very productive and purposeful second half of 2026. Here's a quick summary of what went on in June for me.

Achievements

Personal Studies Progress

My personal studies habit got quite messy over the last year, and I have not done a great job of describing what I have been doing in the last few monthly recaps, so I will take a short moment to put everything back on track here. At about the middle of last year (2025), it became clear that the software development ecosystem was finally locking in to embed AI in its core loop. While I had been exploring the field for about a couple of years already by then, I felt the need to build a more comprehensive and grounded understanding of the possibilities and implications of this shift - so, I picked up two books on the topic to study (AI Engineering and Beyond Vibe Coding).

At the time, I was already studying two other books. This, coupled with a transition to a new team (and organization) within my company, with a whole new tech stack, proved to be too much change in too compressed a timeframe, and so I went through a couple of not very productive months just struggling with too many things to do and progressing too little on each of them. Early this year, I organized myself to address this, and reduced the amount of books I was working through at a time. So, as I mentioned in previous recaps, I finished them one by one, the last one being AI Engineering.

In June I finally reached the stage in which I could move on to pick new books to study. The first one has been Nexus, by Yuval Harari - I have been a huge fan of his work for more than a decade now, having deeply enjoyed Sapiens and Homo Deus, so naturally I wanted to go through the rest of his work. So far, it has been totally worth it, and I will share more of my thoughts once I finish it.

Electron

While I have developed several tools and apps to be used on desktop environments in the past, they have always been restricted to one of three stacks: simple Python apps with TKinter (one of the first programming languages I learned, always feels very comfortable to go back to), Java apps leveraging Swing for the UI (Java being my main professional programming language for cloud and microservices systems, it was always tempting to accept an old and rusty visual presentation in order to do what I consider myself to be best at) or straight out web apps based on HTML and JavaScript running on localhost (made for better visual presentation while still allowing me to leverage over a decade of experience writing cloud systems, but was a very clunky distribution model).

After learning about the open source Pi coding agent, and seeing some creative usage of it embedded in applications through its TypeScript SDK, I got really interested in exploring its ability to embed AI agents within applications. So I used this as an opportunity to finally do something I've been meaning to for several years now: learn Electron. This will allow me to create desktop apps with agentic capabilities, something that gets me really excited. And also, by using Electron I believe I will be able to continue leveraging my knowledge of web frontend technologies while having a much better distribution model for desktop apps.

I went through Stephen Grider's Udemy Electron course to do this, and it was a really great short introduction. Throughout the years, I have taken several of his courses, and his style always resonated a lot with me. The combination of going very deep into the fundamentals of what is going on and getting that understanding, plus having a preference for code-along sessions instead of just showing the final result is an incredibly effective teaching style. Based on my previous experiences I expected this course to do the trick, and it definitely did.

Plans for next month

For July, the main things I expect to achieve are starting the next career-related book in my personal studies (the ideal number for me is 2, one for career and one for hobbies - currently I am working only on Nexus) and continue building personal applications that explore agentic capabilities woven into specific workflows. I am definitely focusing on Electron + Pi at the moment, but I will also try to build a few experiments with LangChain, as that seems to be the standard that the market is converging on for now.

Monthly Recap - 2026-05 May

May continued the intense overtime push of April, but having been operating in this manner for a while, I was able to get more things done even with little free time. Here is a quick summary.

Achievements

Finished AI Engineering book

After restarting AI Engineering, by Chip Huyen, in April, I was able to finish it this month. This is one of the most highly regarded books in this new field, and for good reason. It covers the full lifecycle of AI systems - from data engineering and model selection to deployment, monitoring, and responsible AI practices in production. While I already had some practical experience with several of these topics from building JenAI and other personal projects, the book helped me contextualize and structure that knowledge in a way I had not been able to before. I am glad I was able to finally complete it, and although it goes in depth on some areas that I normally do not touch (such as model training and fine tuning), I feel like this will be a reference I come back to often.

Started using opencode

This month I also started using opencode, an open-source AI-powered coding assistant. I intend to write a dedicated post about this, as I found the experience really intriguing. For now, it suffices to say that agentic coding assistants in general really feel like the next step for AI. Not the end state, but a significant (and positive) advancement in the capabilities that this technology bring to the table. I already used Claude Code to a good extent, but having an open source alternative, which has flexibility to work with several models from different providers, and comes out of the box with free options, democratizes the access to a whole new level. Really excited to explore the implications of this!

Started AI Engineering Track Udemy course

To complement the book studies, I also started the AI Engineering Track on Udemy. This is a more hands-on, practical course with a stronger emphasis on implementation and tooling. I have just started working through it, and my plan is to go through it systematically over the next few months.

Monthly Recap - 2026-04 April

April was a month with very little spare time for me. A huge rush to wrap up the work for the mid-year event of my company meant that every developer worked double shifts during this period. The bright part is that this time I was able to keep up my personal studies habit - although not much else. Here's a quick summary.

Achievements

Restarted AI Engineering

Last year I started studying AI Engineering, by Chip Huyen. This is one of the most highly regarded book in this new field, with some of the most prominent people in software engineering giving it high praise. Due to the several competing priorities along the way, though, I had to stop studying it for several months. Having just finished Beyond Vibe Coding in March, this month I was finally able to restart it. Maintaining consistency with this habit has been a focus for me this year, and I am very glad I was able to stick with it this month, despite the challenges.

Downpoints

Overtime

As I mentioned before, April was a very difficult month with regards to work-life balance. Of course, having to put some extra effort at specific times when the company needs is part of being a professional, but I hope this does not become the new normal for us.

Monthly Recap - 2026-03 March

March was a month of wrapping up ongoing work and exploring new horizons. After a couple of months getting my feet wet with different AI productivity ideas, I was able to finish studying a book I picked up specifically about this topic, as well as presenting a hands-on session at an internal developer conference in my company. Here is a quick summary.

Achievements

InnerSource synergy with agentic AI presentation

This month I had the opportunity to present at an SAP's internal developer conference. It was my second time presenting at this event in a row. I talked about how the InnerSource philosophy and techniques can have a positive synergy with agentic AI software engineering. I think there is a lot of unexplored territory in terms of how we can engineer collaboration in a way that optimizes the value provided by AI and human agents. It was a great experience to share my thoughts and to connect with other developers within the company.

Finished studying Beyond Vibe Coding

I finished studying Beyond Vibe Coding, a book by Addy Osmani about the evolution of software development with generative AI. It goes way beyond the superficial "AI writes code for you" narrative and dives into practical strategies for integrating LLMs into the development workflow in a sustainable and effective way. It covers topics such as prompt engineering patterns, context management, and how to structure projects to leverage AI without losing control of the codebase. While I think it suffers a bit from the pragmatics of this format for the current context (AI tools evolve way too fast for a book cycle - books shine in consolidating the best practices about things that have been around for a while, but for AI it seems that articles are able to cope with the pace of change better), I found it to be a very practical read and will definitely apply some of its insights going forward.

Monthly Recap - 2026-02 February

February was a month for exploration. With the increasing work load at my day job, I used most of my free time to learn and try out a few new things. As expected, all of them related to AI. Here is a quick summary.

Achievements

Productivity tools

While I continue to use JenAI on a daily basis, it remains (by design) a very limited tool. In order to leverage the benefits of AI more widely, I started developing a few more personal productivity tools. The gaps from JenAI that these cover are a simplified persistence model, more intuitive UI and more sophisticated context management. I have also created a few automations to run AI tasks on schedule. As of now, I have no plans to make either of these open source, as they are more exploratory and specific to my work flow.

ComfyUI

Another area of AI that I had not touched for a long while is image generation. While I played around with Stable Diffusion in its early days, and covered image generation on Chargen, it is not something I do regularly. In February I took some time to update myself on newer tools for this, and did a few lessons about ComfyUI. It seemed to me to do a better job of offering enhanced capabilities around the raw image generation models than the other tools I know about, and I look forward to work more with it.

Finished Rich Dad Poor Dad

On a more personal level, I finished studying Rich Dad Poor Dad as part of my personal studies habit. This took longer that I expected, as I started the book roughly in the middle of 2025, but had to interrupt the studies several times along the way. As someone who had never invested before, I really enjoyed the book and found it to be an excellent introduction to the topic.