{ "schema_version": "1.1", "id": "atlas-sandboxing", "slug": "sandboxing", "title": "Sandboxing", "url": "https://feed7.dev/atlas/sandboxing", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "auto-be3ca53745", "slug": "gemini-api-managed-agents-3-6-flash-hooks-and-more-be3ca53745", "url": "https://feed7.dev/p/gemini-api-managed-agents-3-6-flash-hooks-and-more-be3ca53745", "title": "Gemini API Managed Agents: 3.6 Flash, hooks, and more", "why_included": "Use pre- and post-tool hooks for policy checks, and cap autonomous loops with max_total_tokens while preserving resumable state.", "summary": "Gemini Managed Agents now add model selection, sandbox hooks, token caps, schedules, and free-tier access, covering several controls needed for unattended coding work.", "practical_implication": "Add hooks where tool calls need policy checks, linting, or audit output, and set max_total_tokens on autonomous loops. A capped run pauses as incomplete while preserving its environment, so it can resume with a new budget and previous interaction ID.", "agent_context": "Gemini Managed Agents now default to **Gemini 3.6 Flash**. The service adds pre- and post-tool hooks, explicit model selection, scheduled triggers, free-tier access, environment management, and persistent files across scheduled runs.\n\nAdd hooks where tool calls need policy checks, linting, or audit output, and set **max_total_tokens** on autonomous loops. A capped run pauses as incomplete while preserving its environment, so it can resume with a new budget and previous interaction ID.\n\nThe agent remains a preview identified as antigravity-preview-05-2026. Scheduled runs reuse a sandbox, which is useful for stateful work but makes cleanup, state drift, and hook failure behavior important operational concerns.", "source": { "name": "Google", "url": "https://blog.google/innovation-and-ai/technology/developers-tools/expanding-managed-agents-gemini-api-3-6-flash-hooks/", "published_at": "2026-07-28T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "tools", "domains": [ "coding", "security" ], "topics": [ "cloud-agents", "sandboxing", "tool-use" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-28T00:00:00.000Z", "modified_at": "2026-07-28T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/gemini-api-managed-agents-3-6-flash-hooks-and-more-be3ca53745", "json": "https://feed7.dev/p/gemini-api-managed-agents-3-6-flash-hooks-and-more-be3ca53745.json", "markdown": "https://feed7.dev/p/gemini-api-managed-agents-3-6-flash-hooks-and-more-be3ca53745.md" } }, { "schema_version": "1.1", "id": "auto-fb423aadb9", "slug": "run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "url": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "title": "Run multiple isolated agents in a single Sandbox", "why_included": "Give each agent a private Linux user and expose only selected collaboration files through a shared group directory.", "summary": "Vercel Sandbox can now isolate agents as Linux users while exposing selected files through a shared group directory. This gives multi-agent harnesses a simpler permission boundary.", "practical_implication": "Create one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.", "agent_context": "The **@vercel/sandbox SDK** now supports multiple Linux users and groups. Each agent gets a **private home directory**; users cannot read, write, or list one another’s files, while a **shared group directory** supports collaboration.\n\nCreate one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.\n\nThe boundary is user and group permissions inside one Sandbox. The material does not claim separate kernels, resource quotas, or protection from vulnerabilities that cross operating-system user boundaries.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/run-multiple-isolated-agents-in-a-single-sandbox", "published_at": "2026-07-30T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding", "security" ], "topics": [ "sandboxing", "multi-agent", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-30T00:00:00.000Z", "modified_at": "2026-07-30T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "json": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.json", "markdown": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.md" } }, { "schema_version": "1.1", "id": "auto-73fc37ffd6", "slug": "building-a-software-factory-for-ai-sdk-73fc37ffd6", "url": "https://feed7.dev/p/building-a-software-factory-for-ai-sdk-73fc37ffd6", "title": "Building a software factory for AI SDK", "why_included": "Give each agent one reviewable task with isolated context and evidence, while keeping human scrutiny proportional to change risk.", "summary": "Vercel’s AI SDK factory shows a practical scaling pattern: narrow agents produce evidence inside sandboxes while humans retain merge authority and review effort follows risk.", "practical_implication": "The reusable pattern is one agent per reviewable task, each with its own prompt, context, and evals. Start locally, pass evidence between classification, analysis, reproduction, implementation, and review stages, then vary human scrutiny by change risk rather than treating every agent output equally.", "agent_context": "AI SDK had accumulated **over 1,000 issues and almost 800 pull requests** by late June. Four weeks after introducing its factory, Vercel says agents authored **25–35% of merged PRs** and closed **70–80% of issues**, while humans approved every merge.\n\nThe reusable pattern is one agent per reviewable task, each with its own prompt, context, and evals. Start locally, pass evidence between classification, analysis, reproduction, implementation, and review stages, then vary human scrutiny by change risk rather than treating every agent output equally.\n\nThese are early results from one large open-source project, not a controlled comparison. The factory also depends on isolated sandboxes, restricted secrets and networking, queues, monitoring, and sustained human review, so the headline automation rates omit substantial operating machinery.", "source": { "name": "Vercel", "url": "https://vercel.com/blog/building-a-software-factory-for-ai-sdk", "published_at": "2026-08-12T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "agent", "domains": [ "coding", "security" ], "topics": [ "multi-agent", "harness-engineering", "sandboxing" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-12T00:00:00.000Z", "modified_at": "2026-08-12T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/building-a-software-factory-for-ai-sdk-73fc37ffd6", "json": "https://feed7.dev/p/building-a-software-factory-for-ai-sdk-73fc37ffd6.json", "markdown": "https://feed7.dev/p/building-a-software-factory-for-ai-sdk-73fc37ffd6.md" } }, { "schema_version": "1.1", "id": "auto-fec4301fea", "slug": "cloud-agents-start-3x-faster-with-builds-fec4301fea", "url": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea", "title": "Cloud agents start 3x faster with builds", "why_included": "Prebuild deterministic dependencies, start session-fresh services separately, and trace each run to its build and commit SHA.", "summary": "Cursor Cloud Agents can start from continuously prepared environment snapshots instead of reinstalling each session. Internal time to first token improved 3x, with failed builds falling back to the last good state.", "practical_implication": "Builders should move deterministic setup into the install command, keep session-fresh services in the start command, and use team or environment secrets for private registries. Agent runs can be traced to exact builds and commit SHAs.", "agent_context": "Cursor’s new **builds** continuously prepare cloud-agent environments with repositories, dependencies, and install scripts already completed. Internally, environments booted **10x faster** and time to first token improved **3x**.\n\nBuilders should move deterministic setup into the install command, keep session-fresh services in the start command, and use team or environment secrets for private registries. Agent runs can be traced to exact builds and commit SHAs.\n\nThe gains are Cursor’s internal measurements, and user results will depend on repository setup. Builds run hourly by default; failed builds are rejected, leaving agents on the last working snapshot, which may be older than the default branch.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/builds", "published_at": "2026-08-13T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding" ], "topics": [ "cloud-agents", "sandboxing", "observability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-13T00:00:00.000Z", "modified_at": "2026-08-13T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea", "json": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea.json", "markdown": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea.md" } }, { "schema_version": "1.1", "id": "auto-137531afb5", "slug": "apache-maka-137531afb5", "url": "https://feed7.dev/p/apache-maka-137531afb5", "title": "apache/maka", "why_included": "Use its append-only run log and shared runtime to trim context without losing evidence, inspect failures, and recover crashes.", "summary": "Maka gives local coding-agent runs a durable event log, sandbox boundary, recovery path, and shared runtime across desktop, CLI, and evals. It is early, source-only software with changing interfaces.", "practical_implication": "Builders can treat prompts as projections of durable run history: trim old tool output from context without deleting evidence, inspect failures, recover crashes, and compare reproducible experiment cells. Its graph mode also uses isolated Git worktrees for parallel implementation.", "agent_context": "Apache Maka records model messages, tool calls, results, permission decisions, and termination events in an **append-only local log**. Desktop, CLI, TUI, and evaluations execute through one **Runtime Host**, with sandbox approval for file writes and shell commands.\n\nBuilders can treat prompts as projections of durable run history: trim old tool output from context without deleting evidence, inspect failures, recover crashes, and compare reproducible experiment cells. Its graph mode also uses isolated Git worktrees for parallel implementation.\n\nThis is an **Apache Incubator** project with no approved Apache release. The desktop build currently supports **Apple Silicon macOS**; formats and commands may change, Windows is an unsigned preview, and secrets remain plaintext to the local OS account.", "source": { "name": "GitHub", "url": "https://github.com/apache/maka", "published_at": "2026-08-23T00:00:00.000Z" }, "source_class": "tool", "content_type": "GitHub Repo", "layer": "agent", "domains": [ "coding" ], "topics": [ "harness-engineering", "agent-reliability", "sandboxing" ], "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." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-23T00:00:00.000Z", "modified_at": "2026-08-23T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/apache-maka-137531afb5", "json": "https://feed7.dev/p/apache-maka-137531afb5.json", "markdown": "https://feed7.dev/p/apache-maka-137531afb5.md" } }, { "schema_version": "1.1", "id": "auto-b53948be9f", "slug": "give-the-agent-a-budget-not-a-token-sachin-malhotra-anth-b53948be9f", "url": "https://feed7.dev/p/give-the-agent-a-budget-not-a-token-sachin-malhotra-anth-b53948be9f", "title": "Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic", "why_included": "Bound write volume, rate, reversibility, and visibility, with human-only overrides outside the agent session.", "summary": "Production agents need bounded write authority, infrastructure-stamped identity, and human-only overrides; a broad token and tool list cannot control blast radius.", "practical_implication": "Classify write verbs by how failures surface, rate-limit every write, and keep overrides outside agent sessions. Record aggregate behavior with trip wires, size limits using an undo test, and let a trusted proxy stamp agent and session identity.", "agent_context": "A cleanup agent’s empty pipeline stage removed its filter, matching and deleting about **200 workloads** used by **20 engineers** in **90 seconds**. The proposed alternative treats authority as budgets across action volume, rate, reversibility, and visibility.\n\nClassify write verbs by how failures surface, rate-limit every write, and keep overrides outside agent sessions. Record aggregate behavior with trip wires, size limits using an undo test, and let a trusted proxy stamp agent and session identity.\n\nThese are production safety primitives, not a guarantee against bad actions. Limits still need tuning by resource and namespace, while quiet or irreversible operations may require a separate human-held credential.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=rbjWzZK2LU0", "published_at": "2026-08-22T00:00:00.000Z" }, "source_class": "video", "content_type": "Video", "layer": "agent", "domains": [ "coding", "security" ], "topics": [ "harness-engineering", "tool-use", "sandboxing" ], "verification": { "status": "source_linked", "label": "Source Linked", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-22T00:00:00.000Z", "modified_at": "2026-08-22T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/give-the-agent-a-budget-not-a-token-sachin-malhotra-anth-b53948be9f", "json": "https://feed7.dev/p/give-the-agent-a-budget-not-a-token-sachin-malhotra-anth-b53948be9f.json", "markdown": "https://feed7.dev/p/give-the-agent-a-budget-not-a-token-sachin-malhotra-anth-b53948be9f.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "The Implications of Linguistic Illegibility for LLM Security", "url": "https://arxiv.org/abs/2609.02852v1", "source_name": "arXiv", "published_at": "2026-09-02T17:37:22+00:00", "summary": "Language-based monitoring cannot fully reveal an agent’s internal computation, the paper argues. Secure agent runtimes still need isolation and data-flow controls independent of self-reporting." }, { "title": "Guardians of the State: An Air-Gapped AI Fortress for Consumer Data — Rachna Srivastava, DFPI", "url": "https://www.youtube.com/watch?v=2WZsT-znFTQ", "source_name": "AI Engineer", "published_at": "2026-08-29T15:00:25+00:00", "summary": "California’s financial regulator built an offline AI pipeline around replayable data, hardware-backed redaction, model routing, and one-way updates for court-defensible evidence." }, { "title": "Introducing Run SDK: secure eval for your agents", "url": "https://vercel.com/blog/introducing-run", "source_name": "Vercel", "published_at": "2026-08-25T04:00:00+00:00", "summary": "Run SDK executes agent-generated JavaScript or TypeScript behind narrow host functions, with resumable approval and auth boundaries instead of application-level eval access." }, { "title": "What If Your Chip Design Team Moved Like a Single Body? — Abduallah Mohamed, AIDAChip", "url": "https://www.youtube.com/watch?v=0I6aoPSRzVc", "source_name": "AI Engineer", "published_at": "2026-08-22T15:00:25+00:00", "summary": "Tool-by-tool restrictions failed to contain a chip-design agent. Enforce permissions at the substrate, isolate files by role, and propagate approved spec changes from one source of truth." }, { "title": "Unlock Agent Autonomy: The Runtime for AI-Native Systems — Tushar Jain, Docker", "url": "https://www.youtube.com/watch?v=zaGyGgLW3SM", "source_name": "AI Engineer", "published_at": "2026-08-20T16:30:33+00:00", "summary": "Docker argues that agent safety belongs in a model-agnostic runtime: contain each task, grant narrowly scoped capabilities, and judge new access requests outside the agent's trust boundary." }, { "title": "IT Admin for the AI Workforce — Sarthak Aggarwal, Decawork", "url": "https://www.youtube.com/watch?v=q-WOjZhOMCA", "source_name": "AI Engineer", "published_at": "2026-08-20T14:30:38+00:00", "summary": "Agent instructions are not security boundaries. Put identity, scoped capabilities, policy gates, audit receipts, and revocation outside the model before granting production access." }, { "title": "Why Your Enterprise Tech Stack Isn’t Ready for AI Agents — Christopher Lovejoy & Saul Howard", "url": "https://www.youtube.com/watch?v=mav15aW9lLM", "source_name": "AI Engineer", "published_at": "2026-08-19T18:30:15+00:00", "summary": "Enterprise agent requirements should shape the foundation, not be bolted onto a working POC. Design audit, sensitive-data access, human escalation, and evals into the architecture." }, { "title": "Building an Agentic Video Editor for Mass Consumer — Ekaterina Deyneka, Reelful", "url": "https://www.youtube.com/watch?v=pPj_tjlvYjA", "source_name": "AI Engineer", "published_at": "2026-08-18T14:30:38+00:00", "summary": "Reelful maps the coding-agent pattern onto real-footage editing: analyze media, approve a plan, edit as Remotion code in a sandbox, then verify before rendering." }, { "title": "Cline is now available in the AI SDK harness layer", "url": "https://vercel.com/changelog/cline-harness-adapter", "source_name": "Vercel", "published_at": "2026-08-18T00:00:00+00:00", "summary": "Vercel’s AI SDK harness now runs Cline behind the same interface as six other coding-agent runtimes, while keeping Cline in the host and sandboxing only its tools." }, { "title": "Security Firewall for Agents — Ryan Dahl, Deno", "url": "https://www.youtube.com/watch?v=MkRYPFIMCSA", "source_name": "AI Engineer", "published_at": "2026-08-17T18:30:06+00:00", "summary": "Deno treats production agents as untrusted and filters their outbound traffic outside the agent, showing how broad operational access can coexist with protocol-aware controls." }, { "title": "Codex, Behind the Harness — Dominik Kundel, OpenAI", "url": "https://www.youtube.com/watch?v=shRR1e2HXMk", "source_name": "AI Engineer", "published_at": "2026-08-10T16:00:06+00:00", "summary": "Codex exposes several reusable harness patterns: deferred tools, persistent browser execution, sandboxed edits, automated permission review, WebSockets, and server-side compaction." }, { "title": "Multiplayer agentic engineering — Arjun Singh, Superconductor", "url": "https://www.youtube.com/watch?v=OL7kfezynJM", "source_name": "AI Engineer", "published_at": "2026-08-09T20:30:34+00:00", "summary": "Superconductor’s team workflow keeps agent sessions visible across interfaces, runs them in isolated cloud environments, and benchmarks models on its own codebase before routing work." }, { "title": "Vercel AI Gateway and Vercel Sandbox now available on Hermes Agent", "url": "https://vercel.com/changelog/vercel-ai-gateway-and-vercel-sandbox-now-available-on-hermes-agent", "source_name": "Vercel", "published_at": "2026-08-07T19:00:00+00:00", "summary": "Hermes can route inference through Vercel’s model gateway and move command execution into an opt-in cloud microVM, separating model access from the machine where the agent runs." }, { "title": "Give every agent in Herdr its own Vercel Sandbox", "url": "https://vercel.com/changelog/give-every-agent-in-herdr-its-own-vercel-sandbox", "source_name": "Vercel", "published_at": "2026-08-06T23:01:00+00:00", "summary": "Herdr can place each terminal coding agent in a separate Vercel Sandbox, then return its work as an opt-in Git patch instead of letting remote edits touch the local checkout." }, { "title": "Gadgets: Personal app vibe coding that is actually safe — Kenton Varda, Cloudflare", "url": "https://www.youtube.com/watch?v=RmS5s6Wbin4", "source_name": "AI Engineer", "published_at": "2026-08-05T22:42:10+00:00", "summary": "Kenton Varda argues that personal AI-generated apps need per-user code and strong isolation, not one server-owned version. The demo shows agents modifying app code inside a constrained local runtime." }, { "title": "How Factory scaled its cloud backend to one billion monthly requests on Vercel", "url": "https://vercel.com/blog/how-factory-scaled-its-cloud-backend-to-one-billion-monthly-requests-on-vercel", "source_name": "Vercel", "published_at": "2026-08-03T04:00:00+00:00", "summary": "Factory runs API routes, middleware, and webhooks in one Next.js backend at one billion daily requests and 350ms p95. Its agents also deploy internal tools through Vercel APIs under review guardrails." }, { "title": "Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs", "url": "https://www.youtube.com/watch?v=ewtOo0scUh0", "source_name": "AI Engineer", "published_at": "2026-07-31T22:00:06+00:00", "summary": "Post-training gains depend heavily on task selection, rollout quality, and environment design. For many enterprise agents, curated SFT may deliver most of the value before costly RL." }, { "title": "Emulated: The Data for Fully Autonomous Software Engineers and Companies — Joseph Wang", "url": "https://www.youtube.com/watch?v=zkX03APVj0M", "source_name": "AI Engineer", "published_at": "2026-07-31T21:00:03+00:00", "summary": "Repo-only tasks omit deployments, incidents, traffic, and distributed failures. Training infrastructure agents may require multi-node environments with real resources, despite much higher setup cost." }, { "title": "Project-scoped Tokens", "url": "https://vercel.com/changelog/project-scoped-tokens", "source_name": "Vercel", "published_at": "2026-07-30T00:00:00+00:00", "summary": "Vercel tokens can now be restricted to one project, giving agent jobs and automation read/write access without exposing unrelated project, team, or user resources." }, { "title": "How Sandstone grew 40x in 147 days on Vercel", "url": "https://vercel.com/blog/how-sandstone-grew-40x-in-147-days-on-vercel", "source_name": "Vercel", "published_at": "2026-07-27T14:00:00+00:00", "summary": "Sandstone pairs composable agent workflows with preview deployments and private data connectivity, showing the infrastructure a small team used to operate enterprise legal automation." }, { "title": "Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor, Laude Institute", "url": "https://www.youtube.com/watch?v=jRCpXUjz4CI", "source_name": "AI Engineer", "published_at": "2026-07-24T16:00:06+00:00", "summary": "Harbor frames agent development as an empirical loop: run agents in reproducible sandboxes, verify outcomes, inspect trajectories, and evaluate every harness or model change." }, { "title": "From fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI", "url": "https://www.youtube.com/watch?v=OqM67QG_Ikk", "source_name": null, "published_at": null, "summary": "Long-running coding agents need isolated, persistent sandboxes: microVMs limit kernel exposure, while incremental snapshots enable recovery, branching, and faster placement across a fleet." }, { "title": "Claws Out: Securing and Building with OpenClaw - Nick Taylor, Pomerium", "url": "https://www.youtube.com/watch?v=xg1zNlzw7Jk", "source_name": null, "published_at": null, "summary": "OpenClaw’s trusted-proxy mode removes duplicate WebSocket tokens and device pairing, but only if proxy IPs and identity headers are tightly constrained." }, { "title": "TencentCloud/CubeSandbox", "url": "https://github.com/TencentCloud/CubeSandbox", "source_name": null, "published_at": null, "summary": "Tencent Cloud open-sourced a KVM microVM sandbox for agent code execution: sub-60ms cold starts, under 5MB overhead per instance, and E2B SDK compatibility as a self-hostable drop-in." }, { "title": "Quantifying infrastructure noise in agentic coding evals", "url": "https://www.anthropic.com/engineering/infrastructure-noise", "source_name": null, "published_at": null, "summary": "Anthropic reruns Terminal-Bench 2.0 under six resource configs and finds a 6-point score swing from container limits alone — treat sub-3-point leaderboard gaps as noise until the eval setup is documented." }, { "title": "chenyme/grok2api", "url": "https://github.com/chenyme/grok2api", "source_name": null, "published_at": null, "summary": "Grok2API fronts Grok Build, Web, and Console account pools with OpenAI- and Anthropic-compatible APIs, but its unofficial SSO routing creates terms, credential, and renewal risk." }, { "title": "Vercel Sandbox now supports FUSE-based filesystems", "url": "https://vercel.com/changelog/vercel-sandbox-now-supports-fuse-based-filesystems", "source_name": null, "published_at": null, "summary": "Vercel Sandbox can now mount FUSE filesystems — S3 buckets, network shares, any FUSE driver — as POSIX paths, so sandboxed agent code can stream remote data without copying it in first." }, { "title": "Osmantic/ODS", "url": "https://github.com/Osmantic/ODS", "source_name": "GitHub", "published_at": null, "summary": "ODS packages local inference, agents, RAG, workflows, image generation, and operations into one installable stack, reducing homelab integration work at the cost of a large dependency surface." }, { "title": "Scaling Managed Agents: Decoupling the brain from the hands", "url": "https://www.anthropic.com/engineering/managed-agents", "source_name": null, "published_at": null, "summary": "Anthropic details Managed Agents, a hosted long-horizon agent service that separates the harness from its sandboxes — stateless brains, replaceable containers, and a 60% drop in p50 time-to-first-token." } ] }