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Announcement reads
Announcement posts on Yomu, summarised newest first.
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Announcement posts
Sidecars: A low-latency trust boundary for Sandboxes
Modal introduces Sidecars, isolated containers that run alongside a main Sandbox on the same host, establishing a trust boundary between trusted and untrusted code. Sidecars enable communication across trust boundaries at least 3x faster than using separate Sandboxes, reducing latency for operation-heavy agent workloads. The feature addresses the security risk of running an agent’s harness and tool calls together, which creates the “lethal trifecta” of private data, untrusted content, and external communication. Existing solutions either move the harness to a remote service (adding network latency) or rely on egress controls that lack flexibility. Sidecars allow trusted services such as credential injection, proxy traffic, and harness logic to run locally alongside the Sandbox, with independent network policies and dynamic creation via the Modal SDK.
RLTL;DR: Self-Improvement by Internalizing Self-Generated Feedback
Standard reinforcement learning with verifiable rewards (RLVR) fails when tasks are so difficult that agents have no chance of success and no teacher models exist. RLTL;DR addresses this by having the policy write its own TL;DR insight after each failed attempt, conditioning the next rollout on all previous insights. Backpropagation on the in-context insights internalizes a direct task-to-insight mapping. On challenging tool-calling and coding datasets filtered to Pass@128=0, standard GRPO training stays flat at 0-1% Pass@1, while RLTL;DR achieves 14-31% Pass@1 with insights in context during training and 12-13% without at eval time. Reducing to SFTL;DR, training on only 4k (task, insight) tuples without rollouts, recovers almost full performance of RLTL;DR and classical SFT, demonstrating a promising compacted training paradigm.
Introducing SCIM Provisioning in ClickHouse Cloud
ClickHouse Cloud introduces SCIM 2.0 provisioning, making the identity provider the single source of truth for user membership and role assignments. Previously, SAML SSO handled authentication but left user invites and role updates as manual tasks, creating security risks. SCIM provisioning automatically syncs user create, update, and delete operations from the IdP using standard SCIM 2.0 endpoints, maps IdP groups to custom ClickHouse roles via stable external IDs, supports up to two API keys for rotation, and provides audit logging for all provisioning actions. The feature requires an active SAML connection, is limited to verified domain SSO users, excludes system roles like Admin, and revokes all keys if SAML is disabled. It is generally available for Enterprise and BYOC organizations, validated with Okta and Microsoft Entra ID, and eliminates manual invite and deprovisioning tasks.
pg_clickhouse & chdb updates: Encoding, nesting, and types
pg_clickhouse v0.11.0 and the chdb extension v0.1.2 continue their focus on cross-database compatibility between ClickHouse and PostgreSQL with several enhancements derived from header-only C libraries. A new check_encoding server option provides error handlers for invalid byte sequences in text columns: fail, remove, replace, and truncate, with replace displaying the Unicode replacement character for UTF-8 databases. ClickHouse Interval types such as IntervalMillisecond are now imported as PostgreSQL interval columns, and expressions like start_at + duration are pushed down to ClickHouse. Unflattened Nested columns are mapped to two-dimensional text arrays by default, or to arrays of composite types when a structure is specified; parameterized JSON columns are mapped to jsonb with proper ORDER BY pushdown. Additional type mappings include Int128 and UInt256 to numeric, BFloat16 to float4, and spatial types; the deprecated clickhouse_raw_query function was removed, and PostgreSQL 13 support was dropped.
Anyone can start building verified knowledge with Stack Internal
Stack Internal, now generally available, enables anyone to create a free Starter workspace, connect their top three data sources, and collaborate with a couple of teammates to capture and store verified knowledge. The platform evaluates knowledge using five signals—provenance, recency, expertise, corroboration, and human validation—and automatically flags conflicting information at response time to convert team know-how into validated institutional memory. It automatically detects subject matter experts based on topics, and when a critical knowledge gap arises, it notifies the right expert directly in Slack for validation, allowing confirmation or updates within that conversation. An MCP server pushes trusted, verified knowledge into AI tools like Claude, Codex, and Cursor, grounding responses in the organization’s own know-how rather than model guesses. Governance features include custom roles and permissions, full activity tracking with self-serve exportable audit trails, and a knowledge health dashboard displaying metrics like Helpful Answer Rate and Daily Active Users.
Cloudflare Impact reaches $100 million in donations
Cloudflare’s Impact programs have reached $100 million in donated services, protecting journalists, governments, election bodies, and public schools. The programs began as free services for developers and small businesses but evolved after discovering that some free customers doing essential work faced large attacks. Project Galileo, launched in 2014, now includes over 3,500 domains in 120 countries and blocked over 38.5 billion cyberattacks in 2025; a 2026 report found participants faced vulnerability exploit attempts at seven times the average rate. Other programs include the Athenian Project for election websites in 33 U.S. states and eight countries, Cloudflare for Campaigns with over 530 websites, and support for critical infrastructure like public schools and Ukraine. Cloudflare is expanding beyond cybersecurity to help public interest groups adapt to AI, including a hackathon for journalists that produced winning tool AIdas for studying AI bias, and a non-profit startup program.
Small Talk with Emiliano Bonilla, CEO and Co-Founder of Synnax
Emiliano Bonilla, CEO of Synnax, built the company after witnessing rocket engine testing operations ruin multi-million-dollar hardware because Excel and Python could not handle real-time control. Synnax is a platform that replaces decades-old aerospace tools, capturing sensor data in real time and tightening the loop between collection and action. The team built its own real-time programming language, Arc, in about six months after finding existing tools unsuitable. Synnax currently focuses on aerospace but is expanding into cryogenic control for quantum computing, oil and gas, and manufacturing. The company uses JetBrains IDEs, citing their debuggers and merge conflict resolution as critical to balancing speed and quality in mission-critical software.
Cut your AI spend with AI Gateway's Auto Router
Cloudflare released Auto Router in public beta through AI Gateway. Set the model to “cloudflare/auto” and the gateway automatically routes each request to a model capable enough for the task, filtering by credentials, access controls, and provider health. A multi-head classifier on Workers AI assigns probabilities across 14 task categories and rates complexity, ambiguity, stakes, and context dependence on a 1-5 scale. A separate scoring matrix combines those signals with model benchmark results and token prices, applying an adaptive cost penalty and switching costs for cached contexts in long sessions. On an internal general knowledge-work benchmark of 97 tasks with three samples each, Auto Router achieved 86.6% success at $0.0084 per success, costing 35% of Claude Opus 5.5 and 80% of GPT-6 Sol while remaining free during beta.
Detect and send production issues straight to your agent
Cloudflare introduces Issues, a built-in error monitoring feature for the Workers platform now in open beta. It automatically groups repeated exceptions, HTTP 5xx responses, and error logs into a single issue, eliminating manual telemetry linking. Developers enable it by setting observability.issues.enabled to true in wrangler.jsonc, with no SDK required. Issues captures stack traces, console logs, distributed traces, and Worker version, and via automations sends failure context—including user-provided identifiers—directly to a configured coding agent like Claude Code, Cursor, or Devin. The agent receives a structured summary and can further query logs through the Cloudflare MCP to propose code changes and open a pull request. Internally, the Workflows team uncovered and fixed two production bugs within a day using Issues (a migration retry loop and an incomplete deletion process).
Simplifying domains for people and agents
Cloudflare Registrar introduced a redesigned domain search that shows every supported extension and responds progressively as the user types. The search gathers availability evidence from prepared datasets, DNS, and live registry checks, updating only changed results via WebSocket deltas. The system uses Workers, Durable Objects, and Workers KV on Cloudflare’s developer platform to coordinate more than 420 extension checks per query. The Registrar API beta now includes a sandbox for testing, transfer support, and an extensions endpoint with registry requirements. Agents can search, register, or transfer domains through the MCP or the newly launched cf CLI with natural-language prompts.
Monetization Gateway beta: charge AI agents for consumption with HTTP 402
Cloudflare announced the Monetization Gateway closed beta, enabling domain owners to charge AI agents per request for websites, APIs, MCP tools, or datasets. The gateway uses the HTTP 402 Payment Required status code to allow inline payment without redirects or separate payment APIs. Sellers define pricing rules matching request parts and choose from several schemes, with settlement on the Base blockchain using USDC via Coinbase’s x402 Facilitator. Customer use cases include Cloudflare AI Gateway for inference, Ceramic.ai for web search, Stocktwits for stock signals, and API2PDF for document conversion. The beta is available to eligible U.S.-based sellers and buyers, with plans to support additional geographies and payment rails over time.
Pay Per Use: when AI uses your work, you should get paid
AI answer engines can read a publisher’s page and deliver a summary without the reader ever visiting the site, costing the publisher the visit and its associated revenue. Most publishers cannot negotiate individual licensing deals, and no AI company can contract with millions of sites. Pay Per Use, now in beta, is Cloudflare’s system that lets a buyer define a specific use of content and a price, and lets each publisher choose whether to accept that offer. The buyer reports each downstream use through a single Cloudflare API endpoint, and Cloudflare then bills the buyer and pays the publisher monthly. Publishers can track estimated earnings and usage by buyer and domain in the Cloudflare dashboard, with planned future enhancements such as report context like keywords or product type.
Identify AI model overuse with User Insights
Cloudflare AI Gateway’s User Insights now provides context beyond model names and request counts, using a dedicated Worker to classify conversations by task type such as coding, research, summarization, or formatting. The “model overkill” view identifies conversations where a high-capability reasoning model is used for simple tasks, showing which users or agents are driving that usage and the associated cost. Teams can analyze conversation turns to see how many back-and-forth exchanges a task requires, helping uncover inefficiencies in prompts or workflows. The Potential Savings view and Auto Router (closed beta) use task and model-fit signals to automatically route requests to appropriate models without a separate rule for each workload. These features are free for AI Gateway users and require stable user_id and session_id for analysis, with optional Access integration.
Cloudflare Containers, rebuilt to scale agent sandboxes
Cloudflare Containers have been rebuilt to meet the needs of agent sandboxes, which require on-demand creation, fast startup, and the ability to pause and resume. The new durable_object scheduling policy lets application code choose each sandbox’s image and instance type at runtime, replacing the previous deploy-time configuration. A redesigned runtime reduces median container startup from over four seconds to 648 milliseconds, and filesystem snapshots are available in public beta for saving and restoring workspaces. Each container retains its Durable Object as a persistent, programmable controller that manages its lifecycle and outbound traffic. These changes make Containers a more flexible complement to Workers and Durable Objects for agents needing a full Linux workspace.
The Internet has a second audience
More than half of Internet traffic is now automated, and AI agents are the fastest-growing part of it, with daily requests on Cloudflare’s network growing by more than 1,700% over the past year. This second audience consumes bandwidth without generating ad impressions or subscriptions, causing revenue per request to fall while costs rise. Cloudflare is giving site owners tools to see who is visiting, set terms per bot type, and charge for access. These include AI Crawl Control, Web Bot Auth for cryptographically verified agents, Disallow AI Training to block training without losing search indexing, and Pay Per Use to pay publishers when content is actually used. The goal is to build open standards so any site can set its terms and get paid, rather than a few companies controlling discovery and payment.
The Companions to Come
JetBrains is extending Kotlin’s companion mechanism with experimental companion blocks and companion extensions in Kotlin 2.5.0. Companion blocks replace the object keyword with a companion block, compiling members as static members on the JVM. Companion extensions allow adding class-level members to any type, even types the developer does not control. These features improve multiplatform interoperability by enabling expected companion block members to be actualized using Java static members. Companion objects remain fully supported and are still required when the companion must implement an interface, but companion blocks are recommended for new code.
How Atlas Agent Engine Powers MongoDB’s Internal AI Strategy
MongoDB announced the public preview of Atlas Agent Engine, a secure platform for building, deploying, and governing AI agents with built-in memory, retrieval, and open flexibility to choose any LLM or framework. The company validated the platform internally with Holly, a unified agentic workspace that replaces seven disconnected tools for sales reps and is powered by Atlas Agent Engine’s runtime, orchestration, and governance. Compared to manually hopping between tools, Holly cuts the time to research and build an outbound prospecting list by roughly 95%, reducing account prep from hours to minutes. Atlas Agent Engine enforces centralized controls and governance while granting developer autonomy, addressing CIO fears about agents running amok in enterprise systems. It is part of a trio of launches alongside MongoDB 9.0 and Atlas Infinite, giving builders a governed platform to run agents grounded in real-time operational data.
AI Demand is Constant, Unpredictable, and Extreme. MongoDB is Built for It.
AI-driven demand is constant, unpredictable, and extreme, unlike earlier internet and cloud eras where spikes were more predictable. Enterprises need a data layer that can absorb sudden bursts of traffic without re-architecture. MongoDB announced Atlas Infinite, a new deployment option within MongoDB Atlas that independently scales compute and storage within minutes. Atlas Infinite supports replica sets with 10x the data capacity of Atlas Core and delivers 189% more throughput per dollar in internal tests. MongoDB 9.0 was also released, offering up to 35% faster find-one queries and new security features like queryable encryption with prefix, suffix, and substring searches.
Building the Agentic Ecosystem: Partner Updates from MongoDB.local NYC
At MongoDB.local NYC, MongoDB launched Atlas Agent Engine in Public Preview, providing a single foundation for building, deploying, and governing AI agents. Six global systems integrators—Accenture, Capgemini, Cognizant, Infosys, TCS, and Tech Mahindra—are launch partners, integrating the engine into their AI offerings. The platform is model- and framework-neutral, with partners like Arize AI offering trace monitoring for production reliability. MongoDB also announced Devin for MongoDB Modernizations, leveraging Cognition’s AI software engineer to automate legacy code migration while AMP handles data movement. New partnerships with Emergent, Monk.io, and Retool extend MongoDB’s reach into AI application development and internal tools, reinforcing that the agentic era requires an ecosystem of partners with industry expertise.
Context Engineering Has a Retrieval Issue: Introducing rerank-3 and Native Reranking
AI agents require precise context retrieval, not larger windows, and MongoDB’s new Rerank-3 and Rerank-3-lite models outperform Cohere Rerank v4.0 Pro by 2.14% and Qwen3-Reranker-8B by 3.31% overall, with greatest improvements on long documents and code. The $rerank native aggregation stage rescores candidate sets index-free within MongoDB, while Automated Embedding generates and refreshes vectors near-real-time to prevent model version drift. This unified platform eliminates separate vendors and external API calls, supporting lexical, vector, and hybrid search directly against operational data without moving or syncing. The result is simpler retrieval architecture that improved Gravity’s accuracy and cut latency by roughly 50% by better discriminating between strong and weak matches. Retrieval quality forms one layer of MongoDB’s broader agent platform, which also includes performance scaling with Atlas Infinite and agent memory with Atlas Agent Engine.
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