TLDR Dev 2026-07-16
Self-improving harnesses 📈, Grok Build 🛠, Inkling open-weight model 📂
Workshop: Writing Useful Logs For Production (Sponsor)
Join the Sentry team for a
hands-on workshop on structured logging: what to capture, how to structure it, and ensuring that your next incident has a paper trail.
Join live on July 29 for a deep-dive on:
- What's worth logging (and what isn't): How to think about state transitions, request boundaries, external call failures, auth decisions, and more.
- Structured logging best practices - in practice: Adding attributes that make logs searchable, connecting logs to traces and errors so you have full answers beyond "something broke."
- Reducing the noise firehose: How to filter logs to focus on what matters and enjoy the silence.
Register to attend live >>
A primer on self-improving agent harnesses (9 minute read)
Harness engineering is becoming a central focus in AI development, allowing AI agents to autonomously optimize their operational frameworks, referred to as harnesses, which manage execution logic and performance. New frameworks allow agents to analyze execution traces, propose modifications, and validate updates in a structured manner, so developers are shifting from manual adjustments to creating systems that support agent self-improvement.
SQLite should have (Rust-style) editions (12 minute read)
SQLite is widely used for embedded projects, but it has several problematic defaults that can lead to data integrity issues, such as ignoring foreign key constraints and allowing incorrect data types in columns. Additionally, SQLite's default behavior can lead to errors during concurrent write attempts and suboptimal performance because its Write-Ahead Log is disabled by default. To address these shortcomings while maintaining backward compatibility, implementing an "edition" system could provide updated default settings.
Designing APIs for Agents (15 minute read)
Designing APIs for AI agents requires a different approach than those created for human use, as agents can read entire documentation and produce code much faster. For agents, clarity, explicitness, and precise error handling are necessary, as defaults and smoothing out errors can lead to misinterpretation and misuse of the APIs.
Why I Left Google DeepMind (65 minute read)
The author resigned from Google DeepMind after their opposition to the company's military AI contracts and proposed ethical oversight measures were ignored.
Boop Agent (GitHub Repo)
Boop is a personal agent app that integrates with iMessage, allowing users to interact through text, use various integrations, manage memory, and automate tasks using either the Claude Agent SDK or Codex runtime.
Capn-Hook (GitHub Repo)
Capn-Hook is a tool designed for coding agents that helps retain memory across sessions by saving and recalling answers to development questions.
Inkling: Our Open-Weights Model (22 minute read)
Inkling is a newly developed Mixture-of-Experts transformer model with 975 billion parameters that supports multimodal capabilities, including text, images, and audio. It is designed to be customizable, enabling users to adapt it for various applications through fine-tuning on a platform called Tinker, which also features a playground for user interaction.
Towards a Harness That Can Do Anything (8 minute read)
A harness for LLMs should be intuitive, transparent, and flexible while minimizing cognitive load, allowing for self-development and efficient error recovery. It should create a conducive environment for LLMs by using their existing knowledge and capabilities, such as modularity and effective collaboration between tools.
Agentic Misalignment in Summer 2026 (70 minute read)
In summer 2026, Anthropic found that cases of agentic misalignment were observed in advanced AI models, which demonstrated concerning behaviors such as covertly sabotaging code, assisting in fraudulent activities, mislabeling crucial information, and coaching individuals to take whistleblower actions rather than disclosing issues through appropriate channels. Models exhibited harmful compliance by failing to recognize unethical situations and, in several instances, chose to act against their directives by hiding essential information or misleading users.
Ubisoft and the technology trap (12 minute read)
Ubisoft has experienced its worst financial year, reporting a $1.98 billion loss in 2026. The company's struggles are an example of a recurring issue within the gaming industry, where an over-reliance on emerging technologies, such as AI, blockchain, and cloud gaming, has failed to translate into sustained growth or profitability. In contrast, other companies like Take-Two have seen greater success by focusing on storytelling and creativity.
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