Credentio is a newly released, open-source C++ library from Google that allows developers to integrate high-performance, local-first validation of C2PA Content Credentials into their client and server applications. By processing assets entirely locally with a highly optimized memory footprint, the library delivers instant validation verdicts for multi-gigabyte media files without incurring cloud latency, bandwidth costs, or data privacy risks. The library currently features deep manifest parsing alongside configurable trust list integration, and is available now on Google Source with future plans to support full credential generation and embedding.
As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments.
An update on the August 17 outage and the steps we're taking to improve reliability. The post The August 17 outage, and the work ahead appeared first on The GitHub Blog .
Some Framework Laptop 13 owners with last-gen AMD chips have reported that a recent BIOS update is bricking their laptops on both Windows and Linux. The BIOS update causing this issue is version 3.20 for Ryzen 7040-series mainboards, released back in July and still available on Framework's site at the time of this writing. Upon […]
Cybersecurity researchers have disclosed a critical security flaw in isolated-vm, a popular open-source sandbox with more than 2,900 stars and 190 forks on GitHub, that could allow attackers to escape the confines of the isolated environment. The vulnerability ("GHSA-864f-rcv7-6rh4"), which has yet to be assigned a CVE identifier, impacts all versions of the library before and including 7.0.0.
Another day, another new AI agent harness is released. Only this time, it's one that aims to solve a growing enterprise problem as AI agents proliferate: enabling greater developer control of agents and tools, while reducing cost. TrueFoundry , a San Francisco B2B machine learning startup co-founded in 2021 by former Meta engineers, has released its own custom TrueForge harness under the permissive MIT License on Github . Thus, it can be used with any of a developer (or their parent enterprise's) preferred AI models, forked, modified, self-hosted and incorporated into commercial products. The company states in a blog post that when it used TrueForge paired with the open source GLM-5.2 LLM to successfully complete 11 of 14 tasks on DevRev’s Enterprise-Bench — testing multi-step tool use across CRM, issue tracking, and document management systems — it cost 75% less than achieving the same results with Anthropic's Claude Managed Agents harness powered by Claude Opus 4.8 ($2.90 compared to $11.80). Using the same model in each harness, Opus 4.8, TrueFoundry still claims a cost savings of roughly 30% using TrueForge compared to Claude Managed Agents ($8.50 vs $11.80). Why is TrueFoundry giving this powerfully efficient harness away for free? "We’ve had this ask from a bunch of customers," said Anuraag Gutgutia, TrueFoundry’s co-founder and COO, in an exclusive interview with VentureBeat. "You have an ability where you bring in agents and MCPs — can we also get something where you can actually launch these managed agents? I think that is the need we are satisfying. It is not a replacement. People will use this alongside other harnesses, like the cloud-managed ones or the commercial-provider-managed ones, but this will serve as a way for people to use them in a vendor-neutral way and also at a lower cost.” Indeed, TrueFoundry already offers a paid " AI Gateway " for enterprises centrally controlling model and MCP access, credentials, permissions, budgets and observability. TrueForge, by contrast, handles what happens above that gateway: the loop that lets a model repeatedly reason, call tools, receive results and continue working until a task is complete. For enterprise developers, the practical proposition is that they can start locally with a single command and SQLite, then move the same agent harness into a shared deployment using Docker Compose or Helm with Postgres and Redis. TrueFoundry explicitly warns that the local configuration is intended only for use on a developer’s machine, not as an internet-facing production service. Gutgutia said the company ultimately wants its AI Gateway to become the common layer beneath whichever agents and harnesses an enterprise chooses. “There will be a set of companies that will use our harness as the way to launch managed agents,” he said, while others may continue using Claude, other open-source harnesses or internal systems. “But all that traffic should still be flowing through our gateway.” Context management is where TrueForge tries to cut waste TrueForge’s architecture centers on context engineering — controlling how much information gets sent back into the model on every step of an agent run. That includes delaying the loading of MCP tool schemas until they are needed, delegating isolated tasks to subagents, moving oversized tool results into files instead of stuffing them into the active context window, processing structured results through code, and automatically compacting long-running conversations. The documentation sets the default compaction threshold at 50,000 tokens, though it can be changed per agent. TrueForge also treats the sandbox differently from runtimes that keep an agent inside an isolated environment throughout its run. The core agent loop remains on the TrueForge server; a sandbox is provisioned as a tool only when the agent needs to execute code or work with files. TrueFoundry says that reduces unnecessary compute and allows a server to run more agents concurrently. The company argues those choices directly reduce model spending. How TrueForge compares to Claude Managed Agents and other leading orchestration harnesses Type / focus TrueFoundry TrueForge: General-purpose production agent harness designed for enterprise deployments. DeepSeek Harness: Open-source agent harness, currently positioned as a developer preview. OpenAI Codex CLI: Coding-focused agent harness designed primarily for software-engineering workflows. LangChain Deep Agents: General-purpose agent harness built on LangGraph. Anthropic Claude Managed Agents: Fully managed production agent runtime operated by Anthropic. License TrueFoundry TrueForge: MIT. DeepSeek Harness: MIT. OpenAI Codex CLI: Apache 2.0. LangChain Deep Agents: MIT. Anthropic Claude Managed Agents: Proprietary. Price TrueFoundry TrueForge: The open-source harness itself is free. Model, sandbox and infrastructure costs are separate. TrueFoundry also offers an optional commercial governance layer through its broader platform. DeepSeek Harness: No harness license fee. Users separately pay for whatever model providers and infrastructure they use. OpenAI Codex CLI: The CLI is open source. Underlying model/API or subscription costs are separate, OpenAI says around $100–$200 per developer per month, although actual spending varies substantially with model choice LangChain Deep Agents: Open source, with model and infrastructure expenses separate. LangChain also offers optional commercial services through LangSmith. Anthropic Claude Managed Agents: Claude tokens consumed plus $0.08 per running session-hour, with runtime metered to the millisecond. Model flexibility TrueFoundry TrueForge: Vendor-neutral and designed around bring-your-own-model support. DeepSeek Harness: Multi-provider and not restricted to DeepSeek models. OpenAI Codex CLI: Supports configurable inference endpoints, including OpenAI-compatible services and local-model options. LangChain Deep Agents: Broad multi-provider support through the LangChain ecosystem. Anthropic Claude Managed Agents: Claude-centric. Deployment TrueFoundry TrueForge: Can run locally as a single process with SQLite, then move into a production deployment using Docker Compose or Helm with Postgres and Redis. DeepSeek Harness: Designed for local or self-hosted operation. OpenAI Codex CLI: Primarily a local CLI experience, alongside OpenAI-hosted Codex products and services. LangChain Deep Agents: Can be self-hosted or deployed through LangChain and LangSmith infrastructure. Anthropic Claude Managed Agents: Anthropic manages the runtime and infrastructure. Key features TrueFoundry TrueForge: MCP and tool orchestration, subagents, human approval checkpoints, persistent sessions, context compaction, large-result offloading, Code Mode, generative UI, tracing and a sandbox-as-a-tool architecture. DeepSeek Harness: Pluggable models, tools, session storage and agent loops, along with sandboxing, permissions, approval gates and skills. OpenAI Codex CLI: Agent loop, repository and file operations, shell execution, MCP tools, sandboxing, permissions, approvals and context management. LangChain Deep Agents: Planning, subagents, skills, filesystem-based context management, persistent memory, human-in-the-loop controls, MCP support and multiple sandbox backends. Anthropic Claude Managed Agents: Managed execution environments, persistence, tools, sandboxing and infrastructure for long-running agents. Key differentiator TrueFoundry TrueForge: Its strongest distinction is the combination of an open-source, vendor-neutral harness with a clear path from local development to a shared production runtime, plus an optional enterprise governance plane through TrueFoundry. DeepSeek Harness: Emphasizes deep modularity. Major parts of the runtime, including models, tools, storage and the agent loop, are designed to be replaceable plugins. OpenAI Codex CLI: Stands out as a highly developed software-engineering-specific harness rather than a general-purpose enterprise agent server. LangChain Deep Agents: Benefits from the broader LangChain and LangGraph ecosystem and offers a mature open-source path for building general-purpose agents. Anthropic Claude Managed Agents: Minimizes operational burden by having Anthropic manage the runtime, but trades that convenience for tighter model and platform coupling. Open source does not automatically mean governed For enterprise buyers, one of the most important distinctions is between TrueForge by itself and TrueForge connected to TrueFoundry’s commercial AI Gateway. The open-source harness can run independently. But it does not magically inherit an organization’s enterprise access policies on its own. “If you are using just the open source version of our agent harness, yes, you will need to put the right controls therein or in front of some other internal control system,” Gutgutia told VentureBeat. When paired with TrueFoundry’s gateway, the company says agents can inherit the identities and access controls already attached to models, MCP servers, tools, skills and other agents. Gutgutia described the gateway as the place where enterprise SSO, identity providers and granular permissions can be centrally enforced rather than reimplemented separately for every agent. That distinction is likely to be important for platform engineering teams evaluating the project. TrueForge is free software; TrueFoundry’s governance layer is the commercial control plane around it. TrueFoundry says NetApp was a beta user of the harness and contributed requirements during development. Gutgutia said NetApp’s IT organization has used the technology for incident response and faster ticket triage, while also exposing internal agents as self-service tools for developers. He also identified Automattic as an early user. Background on TrueFoundry and its business to date TrueFoundry was founded in 2021 to help enterprises deploy and operate machine-learning models, including Kubernetes-based model serving, training and infrastructure management. Its three co-founders — Nikunj Bajaj, Abhishek Choudhary and Anuraag Gutgutia — previously worked at Meta and WorldQuant, respectively. Gutgutia said the founders' common experience was working around mature systems where infrastructure and controls were designed to prevent costly mistakes — an idea they believed would become increasingly important as AI moved into production inside large companies. As generative AI spread through enterprise software, TrueFoundry expanded from that MLOps foundation toward managing LLM applications and, increasingly, the models, tools and agents around them. By 2025, the company had made its AI Gateway a central part of the business: a layer sitting between enterprise applications and model providers that handles routing, authentication, access controls, observability, budgets, guardrails and failover. That evolution has been backed by roughly $21 million in outside financing. TrueFoundry raised a $19 million Series A in February 2025 led by Intel Capital, with participation from existing investors Eniac Ventures and Peak XV's Surge, as well as Jump Capital and angel investors including Gokul Rajaram and Mohit Aron. The round brought total financing to about $21 million, according to Intel Capital's announcement . At the time, TrueFoundry said its customer base had grown fourfold year over year and that it was managing more than 1,000 clusters for machine-learning workloads. The business has since become increasingly oriented around large-scale enterprise AI traffic. In VentureBeat's January 2026 coverage of TrueFoundry's TrueFailover launch , the company said it had more than 30 paid customers worldwide, had exceeded $1.5 million in annual recurring revenue during the prior year and was processing more than 10 billion requests per month through its AI Gateway. Customers and deployments cited by TrueFoundry have included NetApp, Siemens Healthineers, ResMed, Automation Anywhere, Nvidia, Games24x7 and others; Gutgutia also named NetApp, Siemens, Synopsys and Automation Anywhere among Fortune 1000 organizations working with the company in his interview with VentureBeat. TrueFoundry has also been expanding through acquisition. In June 2026 it acquired UK-based Seldon AI , a longtime MLOps vendor whose Seldon Core software has been used for production model serving and inference. As the acquisition shows, rather than treating traditional ML, LLMs, tools and agents as separate infrastructure categories, TrueFoundry is trying to put them behind a common deployment and governance layer. TrueForge extends that strategy upward into the agent runtime itself. Until now, TrueFoundry's commercial center of gravity has largely been the control plane underneath enterprise AI workloads — deciding which users and applications can access which models and tools, routing requests, enforcing policy, monitoring spend and keeping services available. TrueForge gives the company an open-source runtime above that layer where agents can actually execute. Gutgutia described the relationship as complementary: organizations can run TrueForge independently or continue using other agent harnesses, while TrueFoundry's longer-term business opportunity is to provide the common governance and infrastructure underneath whichever agents enterprises choose.
If you’re juggling multiple Copilot sessions, use the My work pane to track what's in flight, what's done, and what's next. The post GitHub Copilot app for Beginners: Managing your work appeared first on The GitHub Blog .
Attachment 14 of the Apple Developer Program License Agreement has been added to specify updated terms for apps in the European Union, including alternative distribution, alternative payments, and business terms. These terms will go into effect on October 1, 2026. Please review the changes and sign in to your account to accept the updated terms. Translations of the updated agreement will be available on the Apple Developer website within one month.
Ryan welcomes Suneet Malhotra, Senior Manager of Test Engineering at Motorola Solutions, to chat about building end-to-end agentic SDLC pipelines using MCPs, using Cohen’s kappa to evaluate multiple LLMs-as-judges, and how you can improve requirements by shifting QA left through a specification enrichment stage immediately after the design phase.
