{
  "schema_version": "1.0",
  "id": "weekly-2026-07-27",
  "slug": "2026-07-27",
  "issue_number": "003",
  "title": "feed7 Weekly #003",
  "description": "Six practical upgrades for more measurable, maintainable, and cost-aware coding-agent sessions.",
  "published_at": "2026-07-27T00:00:00.000Z",
  "modified_at": "2026-07-24T21:40:09.847Z",
  "url": "https://feed7.dev/weekly/2026-07-27",
  "formats": {
    "html": "https://feed7.dev/weekly/2026-07-27",
    "json": "https://feed7.dev/weekly/2026-07-27.json",
    "markdown": "https://feed7.dev/weekly/2026-07-27.md"
  },
  "selection": {
    "rule": "Six source-backed signals and one distraction to leave out.",
    "mode": "ai",
    "ignore_item_id": "auto-bd6ce42035"
  },
  "items": [
    {
      "schema_version": "1.0",
      "id": "auto-3518047f01",
      "slug": "how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
      "url": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
      "title": "How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads",
      "why_included": "Start with a few core tasks, inspect traces, change one component at a time, and protect a separate test set.",
      "summary": "Production agent behavior should be improved through a loop of tool design, small eval sets, trace review, and calibrated judges—not prompt tweaks based on one bad run.",
      "practical_implication": "Build evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.",
      "agent_context": "The YouTube Ads team recommends optimizing a focused set of agent tools before larger evals, then starting with **a few core tasks** and testing both desired behavior and prohibited actions. Explanations help diagnose multi-dimensional outcomes such as accuracy and brand safety.\n\nBuild evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.\n\nDo not tune against isolated nondeterministic failures. Keep a sparingly used **test set**, refresh it with production data, monitor judge disagreement, and define launch gates before iteration makes the rubric a moving target.",
      "source": {
        "name": "AI Engineer",
        "url": "https://www.youtube.com/watch?v=xyL2Ltkh-SA",
        "published_at": "2026-07-24T00:00:00.000Z"
      },
      "source_class": "video",
      "content_type": "Video",
      "layer": "benchmark",
      "domains": [
        "coding"
      ],
      "topics": [
        "agent-evals",
        "agent-reliability",
        "prompting"
      ],
      "verification": {
        "status": "source_linked",
        "label": "Source Linked",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-24T00:00:00.000Z",
      "modified_at": "2026-07-24T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
        "json": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.json",
        "markdown": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-db6b4984d3",
      "slug": "harness-engineering-is-not-enough-why-software-factories-db6b4984d3",
      "url": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3",
      "title": "Harness Engineering is not Enough: Why Software Factories Fail — Dex Horthy, HumanLayer",
      "why_included": "Agree on design before implementation and build vertical slices so generated changes remain readable and testable.",
      "summary": "Coding-agent loops can raise throughput without preserving maintainability. Keep human ownership of code, and use upfront alignment to make review affordable instead of trying to automate it away.",
      "practical_implication": "Keep humans responsible for the resulting code. Use model-assisted planning, agree on design before implementation, and build in vertical slices so every generated change remains practical to read and test.",
      "agent_context": "The talk argues that coding models are rewarded mainly when **code runs and tests pass**, not when architecture remains easy to change. Review agents and extra loops can raise the floor, but cannot supply a missing maintainability signal.\n\nKeep humans responsible for the resulting code. Use **model-assisted planning**, agree on design before implementation, and build in vertical slices so every generated change remains practical to read and test.\n\nThere is no established benchmark here that proves how well current models preserve codebase quality. Longer-task evaluations such as **SWE Marathon**, DeepSuite, and FrontierCode may help, but model-based quality judges have their own ceiling.",
      "source": {
        "name": "AI Engineer",
        "url": "https://www.youtube.com/watch?v=Ib5GBkD555M",
        "published_at": "2026-07-23T00:00:00.000Z"
      },
      "source_class": "video",
      "content_type": "Video",
      "layer": "agent",
      "domains": [
        "coding"
      ],
      "topics": [
        "harness-engineering",
        "agent-reliability",
        "agent-evals"
      ],
      "verification": {
        "status": "source_linked",
        "label": "Source Linked",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-23T00:00:00.000Z",
      "modified_at": "2026-07-23T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3",
        "json": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3.json",
        "markdown": "https://feed7.dev/p/harness-engineering-is-not-enough-why-software-factories-db6b4984d3.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-bb03b50141",
      "slug": "introducing-cursor-router-bb03b50141",
      "url": "https://feed7.dev/p/introducing-cursor-router-bb03b50141",
      "title": "Introducing Cursor Router",
      "why_included": "Evaluate model routing by cost per shipped change, using repository-specific quality, latency, and code-retention signals.",
      "summary": "Cursor Router classifies coding requests and selects models by task and cost. Cursor reports lower spend in production tests, but the strongest evidence is limited to its own traffic and metrics.",
      "practical_implication": "Teams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.",
      "agent_context": "Cursor Router was trained on **600k+ live requests** and evaluated through A/B tests covering **millions of requests**. Cursor reports frontier-level satisfaction at about **60% lower cost** and offers Intelligence, Balance, and Cost modes.\n\nTeams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.\n\nThe reported **30–50% early-access savings** came from three high-volume accounts and used Opus 4.8 pricing as the counterfactual. Quality relies on Cursor’s satisfaction and code keep-rate signals, so results may not transfer to other harnesses or workloads.",
      "source": {
        "name": "Cursor",
        "url": "https://cursor.com/blog/router",
        "published_at": "2026-07-22T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "tools",
      "domains": [
        "coding"
      ],
      "topics": [
        "model-selection",
        "coding-agents",
        "enterprise"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-22T00:00:00.000Z",
      "modified_at": "2026-07-22T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/introducing-cursor-router-bb03b50141",
        "json": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.json",
        "markdown": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-32d427eb72",
      "slug": "agent-swarms-and-the-new-model-economics-32d427eb72",
      "url": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72",
      "title": "Agent swarms and the new model economics",
      "why_included": "Use a strong planner with bounded cheaper workers, separate contexts, shared decisions, and independent review lenses.",
      "summary": "Cursor’s SQLite experiment suggests swarm harness design can matter more than model mix: separate planner and worker contexts reduced coordination churn while cheaper workers handled most tokens.",
      "practical_implication": "For large agent runs, reserve frontier models for decomposition and decisions, then give bounded implementation work to cheaper workers. Separate their contexts, record shared design decisions, delegate conflict resolution, limit oversized files, and stack independent review lenses.",
      "agent_context": "Cursor’s new planner-worker swarm reached **73%–85%** of a held-out SQLite test suite within four hours, and every new configuration later reached **100%**. Similar quality across model mixes cost from **$1,339 to $10,565**.\n\nFor large agent runs, reserve frontier models for decomposition and decisions, then give bounded implementation work to cheaper workers. Separate their contexts, record shared design decisions, delegate conflict resolution, limit oversized files, and stack independent review lenses.\n\nThis was a controlled but unusual SQLite reimplementation with no source, binary, tests, or internet available to agents. Cursor tested four configurations rather than the full planner-worker matrix, and workers consumed at least **69% of tokens**, so workload shape will strongly affect the economics.",
      "source": {
        "name": "Cursor",
        "url": "https://cursor.com/blog/agent-swarm-model-economics",
        "published_at": "2026-07-20T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "agent",
      "domains": [
        "coding"
      ],
      "topics": [
        "multi-agent",
        "harness-engineering",
        "subagents"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-20T00:00:00.000Z",
      "modified_at": "2026-07-20T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72",
        "json": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72.json",
        "markdown": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-629c51aa89",
      "slug": "citrolabs-ego-lite-629c51aa89",
      "url": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89",
      "title": "citrolabs/ego-lite",
      "why_included": "Isolated browser spaces can let parallel agents reuse authenticated sessions without taking over active tabs on macOS.",
      "summary": "ego lite lets Codex, Claude Code, and other agents automate logged-in web sessions in isolated browser spaces without taking over your active tabs. It is macOS-only today.",
      "practical_implication": "For authenticated browser work, consider whether shared local state and parallel spaces can replace separate automation browsers. The project reports workflows finishing up to 2.5× faster than Vercel’s agent-browser on four complex tasks.",
      "agent_context": "ego lite is a macOS browser that gives each agent an isolated **Space** while retaining access to migrated Chrome logins, cookies, extensions, and bookmarks. Its skill exposes page actions as JavaScript functions.\n\nFor authenticated browser work, consider whether shared local state and parallel spaces can replace separate automation browsers. The project reports workflows finishing **up to 2.5× faster** than Vercel’s agent-browser on four complex tasks.\n\nThe comparison is project-reported and covers only **four tasks**. Windows and Linux remain on the roadmap, and the browser itself is a separate free download rather than part of the MIT-licensed repository.",
      "source": {
        "name": "GitHub",
        "url": "https://github.com/citrolabs/ego-lite",
        "published_at": "2026-07-24T00:00:00.000Z"
      },
      "source_class": "tool",
      "content_type": "GitHub Repo",
      "layer": "tools",
      "domains": [
        "coding"
      ],
      "topics": [
        "computer-use",
        "coding-agents",
        "skills"
      ],
      "verification": {
        "status": "needs_review",
        "label": "Needs Review",
        "method": "unverified",
        "verified_at": null
      },
      "uncertainty": [
        "Automatically selected from source material; feed7 has not independently tested the claim."
      ],
      "lifecycle": "New",
      "published_at": "2026-07-24T00:00:00.000Z",
      "modified_at": "2026-07-24T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89",
        "json": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89.json",
        "markdown": "https://feed7.dev/p/citrolabs-ego-lite-629c51aa89.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-f834153d50",
      "slug": "vercel-mcp-can-now-deploy-code-f834153d50",
      "url": "https://feed7.dev/p/vercel-mcp-can-now-deploy-code-f834153d50",
      "title": "Vercel MCP can now deploy code",
      "why_included": "Make deployment an explicit reviewed final step, and verify the background build rather than treating a returned URL as completion.",
      "summary": "Vercel’s MCP server can deploy agent-built code to new or existing projects and return a shareable URL, extending chat-based coding workflows through delivery.",
      "practical_implication": "Builders using Claude, Cursor, or another MCP client can make deployment an explicit final agent step, but should preserve review and permission boundaries before shipping generated changes.",
      "agent_context": "The Vercel MCP server now exposes **deploy_to_vercel** for deployment to **new or existing projects**. It detects the framework, installs dependencies, builds the code, and returns a shareable URL while the build continues.\n\nBuilders using Claude, Cursor, or another MCP client can make deployment an explicit final agent step, but should preserve review and permission boundaries before shipping generated changes.\n\nThe material does not describe approval controls, rollback behavior, failure handling, or which project settings the tool may change. A returned URL can precede completion of the background build.",
      "source": {
        "name": "Vercel",
        "url": "https://vercel.com/changelog/vercel-mcp-can-now-deploy-code",
        "published_at": "2026-07-23T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "infra",
      "domains": [
        "coding"
      ],
      "topics": [
        "mcp",
        "tool-use",
        "cloud-agents"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-23T00:00:00.000Z",
      "modified_at": "2026-07-23T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/vercel-mcp-can-now-deploy-code-f834153d50",
        "json": "https://feed7.dev/p/vercel-mcp-can-now-deploy-code-f834153d50.json",
        "markdown": "https://feed7.dev/p/vercel-mcp-can-now-deploy-code-f834153d50.md"
      }
    },
    {
      "schema_version": "1.0",
      "id": "auto-bd6ce42035",
      "slug": "building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035",
      "url": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035",
      "title": "Building AI infrastructure with the Effingham County community",
      "why_included": "The infrastructure announcement provides no new Codex capability, access terms, dates, capacity figures, or workflow change.",
      "summary": "OpenAI’s Georgia infrastructure announcement matters mainly as regional expansion and a promise of local Codex access; it offers little operational detail for builders.",
      "practical_implication": "Builders should read this as infrastructure and ecosystem expansion, not as a new Codex capability or workflow change.",
      "agent_context": "OpenAI announced **Project Camellia** in **Effingham County, Georgia**, alongside commitments covering energy use, community investment, employment, and Codex access.\n\nBuilders should read this as infrastructure and ecosystem expansion, not as a new Codex capability or workflow change.\n\nThe provided material gives no dates, capacity figures, access terms, or concrete delivery plan for the stated commitments.",
      "source": {
        "name": "OpenAI",
        "url": "https://openai.com/index/building-ai-infrastructure-with-the-effingham-county-community",
        "published_at": "2026-07-22T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Official Release",
      "layer": "industry",
      "domains": [
        "coding"
      ],
      "topics": [
        "adoption"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "lifecycle": "New",
      "published_at": "2026-07-22T00:00:00.000Z",
      "modified_at": "2026-07-22T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035",
        "json": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035.json",
        "markdown": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035.md"
      }
    }
  ],
  "agent_instruction": "Use these items as source-backed context. Do not invent claims beyond linked material. Prefer practical implications for solo developer work. If sources conflict, call it out."
}