Josh Pigford’s 3-phase AI-agent build process

#1 𝕏

NVIDIA AI launched DynoSim, a full-Rust, workload-driven simulator for the Dynamo serving stack that models your entire inference pipeline on one virtual timeline and screens thousands of deployment configurations in high-fidelity simulation.

#2 𝕏

Clement Delangue hails AI Security Institute’s open release of its evals, datasets and models on Hugging Face, empowering researchers worldwide to scrutinize, reproduce and build on their AI safety work.

#3 𝕏

Guillermo Rauch rolled out per-API Key spend caps on AI Gateway, letting users set budget limits for each key to better control costs.

#4 in

Peter Yang highlights how Josh Pigford—fresh off a $4M exit— is solo-building five AI-agent products, using a 3-phase build process, adversarial code reviews with Opus + GPT-5.5, and a “but for real” AI bug-catching hack.

#5 𝕏

There’s An AI For That launched a free, open-source AI that uses only Wi-Fi signal reflections—no cameras or sensors—to reconstruct real-time, full-body poses through walls, in the dark, and across rooms.

#6 𝕏

Harrison Chase announces that GEPA now integrates with LangChain—thanks to @bryonkuchML’s PR, you can optimize your LangChain chains using the new GEPA adapter; see the walkthrough in the docs.

#7 📝 PromptLayer Blog

How to Build an AI Engineering Stack - Build the stack around workflow requirements, specifying inputs/outputs, latency targets (e.g., <2s for chat autocomplete, <10s for document review), cost targets (<$0.02 per support ticket, <$0.50 per contract analysis), failure tolerance, and audit needs. Implement core layers—product workflow, prompt management with versioning and rollback, model routing (fast/strong/fallback/specialized), context/retrieval tracking of query→chunks→scores→final context, and an evaluation layer with repeatable tests (start with 30–50 examples) plus an eval table recording test ID, input, expected behavior, scoring method, prompt version, model, score, and notes.

#8 𝕏

Teresa Torres highlights Rona Wang’s insight that AI often approaches a “hard ceiling” then stalls at a “soft ceiling.

#9 𝕏

Peter Yang observes that the same Opus 4.8 model yields very different outputs between Claude Code and regular Claude; he finds regular Claude better for writing tasks, likely because its default prompt isn’t optimized for coding.

#10 ▶️

Videos → LinkedIn Posts With AI 🚀 (postiz.com)

SyntaxGTM

#11 ▶️

$21K → $100K MRR in Weeks by Selling to AI Agents 🦞

SyntaxGTM

#12 𝕏

Santiago built a Chromium-based browser that natively runs multiple parallel agents in isolated spaces you can watch, take over, or kill, all without losing your extensions, bookmarks, or logins. It works with any assistant—Claude Code, Codex, Cursor, etc.

#13 𝕏

Julien Chaumond warns that as AI models grow ever more powerful, traditional power structures are scrambling to exert influence within a rapidly closing time window before their sway inevitably declines.

#14 𝕏

Garry Tan lays out two key loops: the empathy loop—deeply understand what users want and deliver it—and the conviction loop—hold firm belief in your product’s value even when others doubt it.

#15 𝕏

Garry Tan says that just as Figma broke past its designer cap to grow design usage by an order of magnitude, this new platform will similarly expand the number of people who touch software by 10×–100×.

#16 in

Udi Menkes introduces “Brain Lift,” a framework that uses Depth of Knowledge levels 1–3 (facts, summaries, insights) as AI context so humans can focus on DOK 4 creative outputs.

#17 in

Marc Baselga highlights Dr. Molly Maloof’s take from our Supra session: the real frontier in consumer health isn’t pooling more labs or sensors but capturing the rich, qualitative patient history.

#18 𝕏

There's An AI For That showcases AI-driven non-contact solutions like camera-free elderly fall detection, location-aware smart home automation, and health monitoring of breathing, movement patterns, and posture.

#19 📝 PromptLayer Blog

How to Build an Anthropic Prompt Generator - Builds a generator that converts a structured input form—illustrated with a "support_ticket_classifier" JSON containing runtime_inputs (ticket_subject, ticket_body, customer_plan), allowed_categories (billing, bug, feature_request, account_access, security, other), output_format "json", constraints ("Return valid JSON only."), bad_answer_examples, and success_criteria—into a strict output schema that includes name, description, variables, evals, and an anthropic_request object (model, max_tokens, temperature, system, messages). It supplies sample prompt-assembly JavaScript to produce a Claude-ready request, emphasizes Anthropic message formatting (system in the top-level system field, user content in messages), and recommends prompt versioning, automated eval cases, and separating policy/developer/task instructions for repeatability and testability.