Something about this year’s @aiDotEngineer World’s Fair just hit different. Last year was the year of “let the agents rip.” This year was th…

Something about this year’s @aiDotEngineer World’s Fair just hit different. Last year was the year of “let the agents rip.” This year was the year of realizing that autonomy without structure creates as much slop as leverage. After a week of workshops, hallway conversations, and late-night patio sessions, here's a summary of my top takways from the event. Thanks so much to @swyx, @mada299 and the entire team for your hard work! Working with LLMS Re-visit and re-implement your skills Each time there’s a new model release, it’s as if you have a kid that grows from middle school to high school. You have to change the curriculum for them to get the benefits of the new model. Define the End State (@trq212) : With Fable, it’s all about defining the end state, and making sure there’s enough context so that the model doesn’t go off the rails and give you something you didn’t want. The model is capable of running for long periods of time, so it just needs enough information so it doesn’t try to invent or do something you don’t want. Be close, but not too close to the bleeding edge (@charlieholtz): If you work too closely to the bleeding edge, you have to spend a bunch of time implementing things that will end up just getting fleshed out better by other companies in the near future. Whenever Claude and Codex come out with a new feature, always try it and get to know it best. Any feature that they decide to end up applying into their harness is a feature work utilizing. Taste still matters a lot (@thaiscbranco_): everyone is basically working with the same type of co-worker (the model). Think about it’s work in the same way. In order to get this super valuable co-worker to produce work that you need for your specific business, you need to spend a lot of time training it on your own taste, your own context, etc, or you’re just going to get generic slop that nobody will want to use. You still need to oversee code quality (@dexhorthy): In the RL process, models tend to “overfit” the code they write. This is because they reward hack inside their RL environments. You need to actively fight this when working with models on your codebase. Model Costs & Selection Open Source Models: Open source models are consistently only 3-9 months behind frontier models, and is finally getting good enough to be used in tandem with frontier models. GLM 5.2 is legitimately a good option now and should be considered for implementing plans, or for completing less intensive tasks. Evaluate models on price per task (@arena): When evaluating models on cost, comparing input/output token prices. Areena AI's model comparison on price per task is probably the best barometer for true cost. Some models are more efficient than others on completing tasks. Memory Management (@RLanceMartin): The most effective memory systems involve providing models with an in-band tool to save memories, then implementing a “dreaming” process for “synthesizing” the memories into a higher-level brain. Sonnet level models are not good at saving meaningful memories, while Opus level models are. Loops, Software factories, and agentic development Create loops, not poops (thanks Sam Hesson): a lot of the discourse around loops on X felt like it was focused on building automated dark style factories. After going to the conference, I realize that loops are definitely a legitimate primitive, but we, as humans, still need to participate in the “outer loop.” If we are not present in the outer loop, then our app will eventually accrue tech debt and crash. Many of the talks focused on how to avoid creating “poop loops.” Think in terms of Orchestras, not Factories (@charlieholtz): Orchestras are a better terminology than factories, because the human still needs to be in the center. You can create your agents, and your loops, but you still need to be in the center, and your judgement still matters for producing things that are meaningful to others. Define different phases of review (@gangdar001 : If certain PRs touch part of the code base that are low stakes, we don’t really need to review them. If PRs touch critical parts of the codebase, then they need much more robust reviews. Stop reviewing raw PRs (@geoffreylitt). There are much more effective methods to reviewing a PR than just looking at the raw code. For example, the Notion guy demonstrated different skills he’s developed that generate HTML documents that help him understand different concepts in a PR more quickly. The quality of your codebase is based on how well you understand it, not the current state of your code. Interfaces & Agent Interaction The terminal is going away. As the industry progresses into the world of software factories, new interfaces will be different, highly experimental and creative. Look to hollywood for inspiration, or what the team is doing at @tldraw Skills & Company Context Skills: There are two core types of skills: skills that address gaps in model performance, and skills on how to perform or achieve certain business outcomes. Portability: it’s important to create a system that allows you to make your skills, and data repositories. Skills, evals, data, wikis, should be portable and pluggable into any agentic harness. Be more un-reasonable (@garrytan, @theo): Stop making trade-offs: Instead of making trade offs between the things that you want to do, why can’t you just do everything? Spend more time leveraging agents: We are only limited by our ability to create loops, and leverage the infinite amount of compute and agentic power that is available to us. There is nothing now that is too crazy to build. Anything can be achievable. What does that mean for what we should do? Trending products and services: @Cloudflare : Seems like Cloudflare has been on a tear offering a lot of core primitives required to create, deploy, and manage agents. Their flue framework looks interesting Ora AI (mentioned by @BurnedChris): A great tool for evaluating our website(s) for agents, and figuring out what we need to do to improve our agentic search capability. @temporalio : Seems like a lot of GTM engineering workflows are using temporal these days to manage ETL pipelines into and outside of their data warehouse. Claude Tag: Apparently Claude Tag is being used heavily at anthropic for software development? It represents a new paradigm shift towards proactive agents. @NotionHQ : Apparently they have allowed for external agents to now communicate back and forth with users inside Notion documents. This is definitely worth exploring. GTM Engineering: Data warehouses: A lot of mature companies have created data warehouses that define the total population, the total addressable market. Need to think about structured vs unstructed data, syncing between systems, merging dirty data. Building vs buy (@jeffzwang): mentioned they just used Salesforce as their core database. They don’t want to spend a bunch of time re-inventing the sales process, and with the CLI it’s pretty good. You should define your workflow, then use as much off the shelf tools as you can to achieve that workflow before attempting to build something from scratch. The best approach: start by automating specific processes inside your organization, and start to build out from there. Don’t spend too much time building out something too massive at the beginning Do a bunch of little things right: The art of GTM is doing a bunch of little things right, and over time, seeing them in aggregate produce a meaningful impact on your web traffic. AI Psychosis & Human Condition (@mikeyk) Verbalizing your emotions to your coworkers can be an extremely effective and underrated way to feel better. Take Breaks: Lots of people are burning out right now. Don’t let it happen to you. Fight against that by taking regular days of (received tons of applause with Anthropic mentioned this on the main stage) Focus on cultivating your creativity: In the world of AI and being able to get lots of work done, human creativity is becoming more valuable as a skill, because it’s something that agents can never be good at. One way to be creative is to pursue difficult problems. You will have to be creative in order to figure out how to solve them. Understanding = compounding (@geoffreylitt): It’s important to understand what you’re agents are doing, so that your knowledge compounds over time. If you just let your agents continue working, you will eventually hit a wall, and will not be able to provide your agents with the guidance they need.

Source: Yohei Nakajima (X) | 2026-07-03