Recommendation · MoneyViren Baraiya recommends that for payments you use a very well-defined deterministic workflow rather than non-deterministic agent behavior. Similarly, for an agent harness responsible for monitoring production deployment and managing Kubernetes clusters, define a very well-defined workflow for the sequence of steps to execute when restarting a cluster, so that every cluster restart is exactly the same. The harness should deliver determinism when it matters.
AI Engineer · Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Recommendation · MoneyViren Baraiya recommends that restarting a production cluster should require a human gate: send a Slack message to somebody saying 'I'm going to restart this cluster, do you think it's okay to do that or not?' He does not want this to be left to a hallucination by an LLM — execution guarantees must be deterministic, not left to an LLM deciding it doesn't need to do it.
AI Engineer · Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Recommendation · MoneyGabriel Martinez recommends that engineers read the code and test the behavior themselves rather than accepting output, own the trade-offs of the code they merge, and notice the missing requirements and decide whether a thing deserves to be part of the system. He also recommends not merging code you don't fully understand or haven't tested yourself.
Creator advice attributed to Gabriel Martinez.
AI Engineer · Generation Is Cheap, Review Is Expensive: How to Stop Shipping AI Slop — Gabriel Martinez, G2i
Recommendation · MoneyGabriel Martinez recommends spending time thinking about guardrails for agents, coding conventions, architectural styles, and constraints around how the system should be shaped. He frames this as fighting slop with conventions, standards, documented flows, small reviewable surfaces, and clear ownership boundaries and definitions of done.
Creator advice attributed to Gabriel Martinez.
AI Engineer · Generation Is Cheap, Review Is Expensive: How to Stop Shipping AI Slop — Gabriel Martinez, G2i
Method · MoneyPostman built an API context graph by: (1) identifying and cataloging every single microservice on production, (2) identifying every endpoint exposed by each REST API service, (3) capturing how each endpoint is implemented right down to the exact line of code, (4) mapping how each service talks to each other and how each endpoint connects to every other endpoint, (5) tracking how data flows all the way down across the stack including databases and caches, (6) identifying how applications like front-ends, CLIs, and APIs interact with the back-end architecture, and (7) indexing all of this data using an LLM to produce a centralized and effective context graph layer.
endorsed
Method, not a verified protocol.
AI Engineer · We Mapped 115 Microservices for Our Coding Agents — Kamalakannan Nandagopal, Postman
Method · MoneyWhen a PR is created, the agent reviews it and posts issues and inline comments; it also analyzes CI failures and posts a summary of root causes, being particularly good at catching flaky tests; the agent can automatically fix code review issues and CI failures, and can be set up to automatically loop until all issues in a PR are fixed, producing a green PR ready to merge.
Method, not a verified protocol.
AI Engineer · AI Writes More PRs. Who Validates Them? — Ali-Reza Adl-Tabatabai, Sonar
Recommendation · MoneyWill Lyon recommends running agent messages (user and assistant) through an entity extraction process that identifies entities and how they are connected, converting unstructured data into a knowledge graph.
AI Engineer · Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j
Recommendation · MoneyWill Lyon recommends distilling a workspace- or project-scoped context graph into a skill grounded in actual observed data rather than just a markdown file, with deterministic, well-understood procedural steps in the graph.
AI Engineer · Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j
Recommendation · MoneyWill Lyon recommends a knowledge graph with strong types and relationships describing how entities are related, and stresses that the entity resolution phase — ensuring a canonical representation of a thing, like knowing Dr. Nguyen is a provider with a name and role — is one of the most important parts of building the graph.
AI Engineer · Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j
Method · MoneyMatt Gibiec describes a workflow: start by observing your environment to build context and dependency mapping; provide production data and details locally to developers as they test; when a service deviates from normal (slower or throwing new errors), inform the developers' agents in the IDE; integrate the tooling in the code repository so it has context of what changed, and overlay change data with issue data to attribute issues to pushed changes or unsuccessful merges; when issues are detected, suggest a fix that uses the context of your environment, application, and monitored agents — not just a fix for the particular breaking issue; and drive the entire SDLC by providing the fix while giving you control at the end to decide whether to push the feature or fix out.
Method, not a verified protocol.
AI Engineer · Your AI Agent Has No Nervous System — Matt Gibiec, Dynatrace
MethodFedus describes how Periodic Labs builds RL environments from its own data: timestamp the state of experimental evidence at each date so you can reconstruct what was known; build environments asking, given this body of evidence and the choices, what was the next choice the scientist made or the outcome of that experiment; for phase-identification environments, reward correct identification of phases actually present and penalize spurious phases or phases with no chemical plausibility; and maintain lineage of the whole experimental process, which enables constructing RL environments that aren't plausible elsewhere.
Method, not a verified protocol.
Latent.Space · Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk
Recommendation · MoneyMatt Owens recommends putting yourself in a fast-growing environment shortly after school — a small startup where you can hire, collaborate, and learn from designers, developers, and business people all figuring it out together. He describes working 14 hours a day at Method 5, a startup that grew from 7 to 45 people in about a year and a half, calling it a 'pressure cooker' that was a blessing after grad school.
Design Better · Matt Owens: Athletics co-founder on keeping your creative fire lit
Method · MoneyThe described memory-learning system works as a loop: when an agent runs, its context is loaded from a context hub (instructions, skills, markdown files, policies, rules). When the agent is invoked, a trace is produced showing failed tool calls, user corrections, and deviations such as tone straying from the desired tone. An engine then looks for patterns in those traces and identifies where static skill markdown files in the context hub need to be changed or promoted, and makes that change. After the memory update process runs, the next time the agent runs its tone visibly changes in interactions with users.
described as the speaker's system; behavior change observed by the speaker, not benchmarked
Method, not a verified protocol.
AI Engineer · Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain
Recommendation · MoneyAn important consideration in self-updating agents (like OpenClaw and Hermes) is deciding what to make self-updateable versus what needs human review. Procedural memory — instructions, policies, and tone guidelines — probably drives about 75% of an agent's behavior, so changes to it should be surfaced to a human before being committed to the agent's hot reload path.
the 75% figure is the speaker's own estimate for his agent, not a measured benchmark
AI Engineer · Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain
Recommendation · MoneyWhen tools return large data (logs, database results): externalize by moving big data to persistent storage with a memory pointer, select by retrieving only what is relevant, and compress by summarizing/compacting information.
AI Engineer · Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS
Recommendation · MoneyBuild a two-tool pattern: one tool fetches the large data (e.g., application logs) and saves it to storage, returning a memory pointer ID; a second tool retrieves the data by ID only when needed. Don't put large tool outputs into the context window on every invocation. When the agent actually needs the raw data, it presents the stored ID to retrieve it.
AI Engineer · Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS
Recommendation · BodyDr. Morin advises that on acceleration day, you focus only on the acceleration — everything afterwards is not important. When coaches cue and emphasize everything every time in sprinting, there is no way to get quality over the entire spectrum — this is the main problem of beginning coaches. He structures one weekly session focused on acceleration and two sessions focused on high-speed running, with the second high-speed session being a hamstring prevention session rather than a max-velocity session. For acceleration work, he uses about the same metrics as high-speed work (100-150 meters of exposure), noting it's a principle, not a recipe.
Consequential creator advice requires independent verification before Today can promote it.
Garage Strength · Why Most Athletes Train Speed Wrong w/ Sprint Scientist Dr. Jean-Benoit Morin
Recommendation · BodyFor athletes who must stay in bent positions (like point guards), you would never tell them to drive less horizontally because it is dangerous for the hamstrings; instead, teach them to be much stronger with their trunk and hamstrings in exercises that stimulate highly bent positions.
Garage Strength · Why Most Athletes Train Speed Wrong w/ Sprint Scientist Dr. Jean-Benoit Morin
Recommendation · BodyDr. Morin advises that when an athlete shows good top speed, strength, and broad jump but weak acceleration, the first step is to assess acceleration objectively with metrics — maximum force output and maximum propulsive power output on the ground. You can also bring kinematic analysis including contact time to diagnose acceleration weaknesses such as trunk collapse or overly vertical push. He stresses combining quantitative numbers with qualitative movement assessment — how does he move? He also notes that changing the trunk angle during acceleration likely addresses braking issues in the first three to seven steps (per the host's example with a running back), and that an athlete already elite at high jump will very likely not improve acceleration by jumping higher.
Garage Strength · Why Most Athletes Train Speed Wrong w/ Sprint Scientist Dr. Jean-Benoit Morin
Recommendation · BodyDr. Morin recommends that for athletes who must stay in bent positions for their sport (like running backs and basketball point guards), rather than changing their running technique, you strengthen the trunk and hamstrings in highly bent positions. He advises using very intense trunk flexion exercises, choosing exercises that involve both trunk flexion and huge hip flexion, and using unilateral exercises as much as possible to replicate the lunge and sprint movement. He cites the drop and catch RDL as an example of a very intense trunk and hip flexion exercise, and notes resisted sprinting is useful because it forces the athlete to be more bent forward with higher hip flexion, strengthening the posterior chain with kinematics close to future actions. The same bent-position strengthening is
Garage Strength · Why Most Athletes Train Speed Wrong w/ Sprint Scientist Dr. Jean-Benoit Morin
