An OS sandbox is the kernel boundary beyond path policy. looplane now ships a fail-closed CommandSandbox: sandbox-exec on macOS, Landlock plus seccomp on Linux, and exit 126 when containment cannot be proven. Coverage still focuses on verification commands, and external CI confirmation remains open.
Looplane's prompt is now `m3-exact-edit-v4`: the version persists into artifacts; core/tool/interaction/runtime/instructions/skills/workspace/memory are composed as stable or dynamic sections; and positive/negative examples cover replace_text, unified diffs, and direct replies. Unit tests pin the structure, while live-eval coverage still needs expansion.
Intermittent NVIDIA NIM 500s exposed Looplane's early gap: classified errors with no retry consumer. SDK retries are now disabled; the harness gives each candidate up to five attempts with jittered exponential backoff and capped Retry-After handling, then can move to an explicitly configured fallback model. Both model.retry and model.fallback enter the event log.
looplane now connects events.jsonl to a deterministic reducer, CLI timeline, canonical JSON, SDK replay, and safe event-point forks. Forking never replays prior tools or model calls; provider/live-runtime validation, redaction, and richer replay hooks remain open.
Mature subagents need roles, bounded fan-out, narrowed permissions, and a result contract. looplane now has native named-role schedules, parallel fan-out, child allowed_paths constrained by the parent, unsafe execution disabled by default, and parent-approved transaction proposals. Persistent background lifecycles, recursion trees, and automatic worktree merging remain open.
looplane now has CostBreakdown, an explicitly estimated static GPT-5-family price table, per-lane usage/cost, and OTel cost fields. Unknown models still show tokens without invented dollars; broader pricing coverage, authoritative external-CLI bills, and live billing reconciliation remain open.
Looplane's core surface has grown from seven tools to nine with a read-only `tool_program` and rollback-capable `tool_transaction`; search prefers ripgrep and arbitrary shell remains absent. Native MCP tools join only from allowlisted servers and default to execute approval without trusted read-only metadata.
Looplane's disposable clone and SafePathPolicy protect the source repo. `--sandbox-checks` can now wrap verification commands with macOS sandbox-exec, Linux bubblewrap, or Landlock, while Cloudflare provides a separate bounded Sandbox slice. Network policy, external-runtime coverage, and production hardening are not yet consistent across those backends.
The integration post (order 20) of the Sandbox Series. How MCP Server runs agent code in a sandbox, where the isolation boundary lies, common vulnerabilities (CVE-2026-33032) and defense strategies.
The cloud deep dive post (order 12) of the Sandbox Series. Modal Sandboxes architecture, GPU sandbox support, pricing model, and how it grew from a feature to a $300M–$1B annualized run rate standalone product line.
Keenable.ai positions itself as search infrastructure for AI agents: a 100B+ document index, Search/Fetch APIs, MCP/CLI entry points, 100K free monthly requests, and keyless public endpoints. It is worth tracking, but the 100B+ index, latency, and quality claims are still mostly company-provided; NEEDLE is open, but needs external reruns and human review.
screenshot-to-code is not a one-shot screenshot-to-HTML tool. Its core is a 30-step Agent Loop with 7 tools — extracting real assets from screenshots, self-verifying with Playwright, and running 4 models in parallel so users pick the best output. 74,500+ GitHub stars, MIT License.
Warp's self-improving agent pattern is not about dumping every mistake into a prompt. A base skill does the work, humans leave feedback in GitHub or Slack, an improver skill turns repeated signals into a small diff, and humans review the PR before the next run inherits it.
TinyFish provides four web APIs for AI agents: Search, Fetch, Agent, and Browser. Search and Fetch are permanently priced at $0 with no credit card requirement, making them a practical default layer for RAG and document retrieval.
Four philosophies of ADE workspaces: arul28/ADE's Brain+Lane, Superset's 100-agent IDE, Herdr's Rust-native runtime, and Kadro/Orca's pane and Fleet angles — compared in one table with a decision tree for when to pick a workspace over an Omnigent-style control plane.
Omnigent moves governance off prompts into a Server-side Policy engine: Python functions returning allow/deny/ask, a three-layer stack with cost budgets and tool caps, plus Omnibox OS-native isolation via bwrap/seatbelt and egress credential brokering — compared with five peer governance stacks.
The same model can score 20 points apart on different harnesses, 32% of SWE-bench Pro verifier judgments were found to be wrong, and DeepSWE's 113 tasks make most models score zero. This guide decodes six major coding benchmarks — what they test, which are easy to game, and which ones you should care about.
CS50 AI covers the introductory material of artificial intelligence in seven weeks and twelve Python projects: search, logical inference, probability, optimization, machine learning, neural networks, and language. In the first four weeks a person writes the problem down precisely; in the last three the computer learns from data. Lectures, notes, projects, and autograding are free and open to people outside Harvard. This post is the entry to the series: the mechanism each week teaches, an example you can work through, a prerequisite check, and a reading path.
The same model can cost 2-5× more depending on the channel. Direct API is simplest, aggregators (OpenRouter) are most flexible, cloud platforms (Bedrock/Vertex) suit enterprises. This post compares actual August 2026 prices across six channels with a decision tree.
meta-harness means two things: Databricks' control plane and Stanford's outer-loop optimizer. This post uses a four-layer model (MCP/ACP/Runtime/meta-harness) to place Omnigent, Zed ACP, Vercel HarnessAgent and Cloudflare Flue.