The Linux Foundation has announced that it is taking over TRACE, an open-source standard used for AI runtime attestation, according to a Techub News brief citing Crypto Briefing. The move is aimed at improving accountability in AI systems and supporting trust and compliance across different computing environments. The item did not disclose additional technical details about TRACE’s governance structure, implementation roadmap, or participating organizations. Based on the source text, the announcement centers on the Linux Foundation’s role in assuming stewardship of the standard and the intended goal of making AI systems more accountable at runtime. The report also frames the handover as a step tied to broader trust and compliance needs in cross-environment computing scenarios.

Researchers from the University of California, Berkeley and the University of Texas at Austin have introduced FreeToken, an edge-native mixture-of-experts inference engine designed to turn personal computers into a unified elastic inference platform. According to the release cited by Techub, the system can run the 753B-parameter GLM-5.2 model on a single workstation GPU, a 284B model on a gaming desktop, and a 35B model at interactive speed on a laptop GPU with 8GB of VRAM. FreeToken has been open-sourced under the Apache-2.0 license on GitHub and published on PyPI. The team also provides one-click desktop applications for Windows and Linux. Its command-line interface supports Linux x86_64 systems and NVIDIA GPUs, and the ft serve command can expose an API endpoint on port 1919 that is compatible with OpenAI and Anthropic. The project is aimed at individual developers, startups, and engineering teams at small and medium-sized businesses, with a focus on privacy-sensitive use cases such as healthcare, legal work, defense, finance, and intellectual-property-heavy R&D. Example applications include local coding agents, private code review, offline contract analysis, and synthetic data generation.

David Heinemeier Hansson, the creator of Ruby on Rails and co-founder of 37signals, has announced the launch of the nonprofit Omacom Foundation with $8 million in initial funding. The organization is intended to provide long-term support for infrastructure tied to Omarchy, as well as open-source development connected to the project and its dependencies. According to the announcement, the foundation will hold relevant trademarks, cover infrastructure costs, and fund dependent open-source projects and developers. The first round of funding came from eight donors contributing $1 million each, including Shopify CEO Tobi Lütke, Stripe CEO Patrick Collison, Dell Technologies CEO Michael Dell, Block Chairman Jack Dorsey, Cloudflare CEO Matthew Prince, Oculus co-founder Brendan Iribe, 37signals CEO Jason Fried, and Hansson himself. The foundation will also become the exclusive sponsor of Hyprland under a three-year agreement with an option to extend for another two years. Starting Oct. 10, it will take over sponsorship, end the existing paid Hyprperks subscription, and make related services free while still accepting individual donations.

The theft tied to the Coldcard hardware wallet has revived a long-running argument in the crypto sector: publishing code is not the same as delivering open-source security. In the article translated by Foresight News from Juan Galt, the incident is used to draw a sharp line between free and open-source software, source-available licensing, and the economic incentives that determine whether anyone actually audits code. The piece says users lost more than $100 million in bitcoin, or over 1,500 BTC, and argues that the event exposed a widespread misunderstanding inside the Bitcoin community itself. It walks through the Free Software Foundation’s four freedoms and the Open Source Initiative’s standards, then points out that Coldcard’s firmware includes license restrictions that bar certain commercial use. That means, by the article’s framing, it should not be described as open source in the formal sense. The report also contrasts that structure with Bitcoin Core’s public development model, where code review, open discussion, and long review histories shape decision-making. It argues that open source creates the possibility of verification, but not verification itself. The final section focuses on AI, saying new models are making large-scale code review possible while also increasing pressure on maintainers and eroding the old security advantage of closed-source software.

Open-source AI company Nous Research has updated its hiring page with eight categories of full-time openings, offering a clearer look at how it is building out Hermes Agent beyond model research. The roles span AI evaluation, cloud infrastructure, product analytics, security, enterprise deployment and UI/UX design, indicating that the company is adding both engineering depth and commercial execution capacity around its agent product. The listings show Hermes Agent moving on several fronts at once. Nous Research is hiring a Machine Learning Engineer focused on evals, including benchmark design, LLM-as-a-Judge systems, weekly regression testing and red-team workflows. It is also recruiting a Forward Deployed Engineer to install Hermes Agent Enterprise in customer environments, integrating enterprise APIs, internal data sources, authentication systems and business applications across cloud, on-premises and hybrid architectures. At the same time, the company is expanding Hermes Cloud, which is now in preview and positioned as a way to run AI agents as always-on cloud services with persistent memory, scheduling, automation and isolated sandboxing. Product, analytics and design roles point to a broader push into desktop, web, mobile and terminal experiences. Nous Research also says candidates without a matching listed role can still pitch how they want to contribute and submit materials directly.

A new analysis from Bitcoin Magazine argues that the Coldcard hardware wallet incident exposed a core misconception in Bitcoin software: publicly readable code is not the same thing as open-source software. The article says users lost more than $100 million in bitcoin, over 1,500 BTC, after a critical entropy flaw in Coldcard firmware went unnoticed for roughly five years. That flaw, it argues, showed how software safety depends less on source visibility alone and more on who has the legal and economic incentive to review code closely. The piece draws a sharp distinction between Free and Open Source Software standards and “source-available” licensing. It notes that Coldcard firmware was released under MIT terms plus the Commons Clause, which removes the right to sell the software commercially. Because of that restriction, the article says the software does not meet the Open Source Initiative standard. It contrasts that model with Bitcoin Core, whose MIT-licensed code, public review process, contributor structure, nonprofit funding base, and long-running public discussions are presented as a large-scale example of open-source development working as intended. The article also says AI is changing the security equation on both sides. It cites the volunteer Bitcoin Red Team, backed by OpenSats, which used frontier AI models to scan hundreds of Bitcoin repositories and reported thousands of findings, including dozens rated critical or high severity. At the same time, it argues that AI-generated code is adding strain to maintainers and weakening the old security advantage once associated with closed-source software.

Nearly 2,000 compromised WordPress sites were used as part of a broader cybercriminal operation that distributed malware, stole files, monitored victims, and deployed ransomware, according to a report from Check Point Research published Tuesday. The firm said it first identified the StopAndProtect ransomware family in mid-May and later linked it to a larger toolkit-driven campaign rather than a single malware strain. Researchers said the operation targeted Windows users through fake CAPTCHA prompts on hacked websites, tricking victims into running a PowerShell command that installed malware capable of stealing credentials, harvesting cryptocurrency wallet seed phrases, spreading through networks and USB drives, locking screens, and dropping ransomware. Check Point said operational security failures by the attackers exposed internal files, infection logs, screenshots from victim machines, and source code used to manage compromised websites at scale. By July 24, the campaign had affected more than 6,000 unique IP addresses, including 1,852 in the United States and 630 each in Russia and India. The researchers also collected more than 31,000 screenshots and over 700 archives containing stolen documents, passwords, and crypto wallet files.

A long-form piece published by TechFlowPost argues that crypto venture capital is not disappearing after the speculative boom. It is being repriced. The article says the market now shows a split between strong top-line industry data and weak early-stage liquidity: institutions hold more than $175 billion in crypto assets through exchange-traded products, onchain projects generated $11 billion in fees over the last 12 months, and the sector logged $8.6 billion in M&A plus 11 IPOs. Yet Galaxy Research data cited in the piece shows only eight new VC funds launched last quarter, the lowest level since 2020, while quarterly investment fell to $4 billion, or roughly $16 billion annualized, about half of 2021’s $31 billion pace. The authors trace the problem to a crypto funding model built around early token listings and quick liquidity rather than durable business value. They argue that many token models failed because projects lacked real business models and token holders had no legal claim on operating income. In their view, the industry is now moving toward structures that tie revenue to tokens, including buybacks, while also reopening other exit routes such as acquisitions and IPOs. The article identifies three sectors that have already reached sustainable product-market fit: stablecoins, prediction markets and onchain perpetuals. It also points to tokenized Treasuries, tokenized equities, machine payments, onchain credit and compliance infrastructure as areas where early-stage opportunities may now be forming. The broader conclusion is that crypto investing is shifting away from broad thematic betting and toward specialized, patient capital focused on business quality, regulation and long holding cycles.