A people-search tool that promises “private and secure” searches left more than 9 million image files exposed online, including photos of people’s faces. A security researcher found the unprotected database, according to reports by WIRED and Digital Trends.

The tool is ClarityCheck, a service that lets someone upload a photo to identify the person in it. Its website tells users that “your reverse image search is private and secure”. The researcher, Jeremiah Fowler, published his findings through ExpressVPN. Fowler is known for finding unsecured databases left open on the internet.

The exposed store held about 450GB of data and needed no password to open, Fowler reported. That is a large trove for a mid-sized consumer service. Many files sat in folders labelled “faces” and “profiles”. They included profile photos, screenshots and other images of adults, teenagers and children.

Fowler put the total at more than 9 million files. The scale is what stands out: a single misconfiguration left millions of faces reachable to anyone with the link.

How it was exposed

The storage was reachable through a web address found in ClarityCheck’s own public website code, according to the reports. Search engines did not index that link, but the files behind it had no lock. The company has since restricted access, the reports said. Fowler notified ClarityCheck, and the company locked the store down afterwards.

Neither report said how long the data had been open before he found it. Fowler said he could not tell whether anyone else had downloaded the files, since the storage kept no public access logs he could review.

There was a second problem too. By changing certain ClarityCheck web addresses and entering a person’s name, someone could surface possible email addresses, phone numbers and physical addresses, WIRED reported. That required no special access. It is a separate weakness from the exposed image store, and it points at the service’s own lookup system rather than a storage bucket.

Fowler found no sign that anyone reached the database with bad intent before the company secured it, the reports said. So far, they have not connected the exposed contact details directly to the exposed photos. The two problems were separate misconfigurations, the reports said, one for the images and one for the personal details.

Why this is sensitive

The nature of the service makes the lapse awkward. ClarityCheck is built to help people check strangers and decide whom to trust online. A leak of its users’ own uploads cuts against that promise. The service asks people to trust it with the very images they use to vet others. ClarityCheck markets the search as a way to spot catfishing and fake dating profiles, according to Digital Trends.

Digital Trends noted that a lapse involving its own users’ uploads is a particularly uncomfortable one for a service sold on trust and safety.

The people in the photos may never have used the tool. ClarityCheck lets a user upload someone else’s face to search for them. That means a person whose face sits in the database may have had no contact with the service at all.

Some of the images appeared to come from social media, dating profiles and screenshots, Fowler reported. That raises the prospect that people had no idea their faces sat in the store.

He also said he found files with timestamps beyond ClarityCheck’s stated 14-day limit for keeping uploaded images. If accurate, that would suggest the service held some images longer than its own policy allows. ClarityCheck did not address the retention point in its statement, the reports said.

What ClarityCheck says

ClarityCheck disputed the description of the store as “publicly exposed”. In a statement to WIRED, the company argued that reaching the files required a specific, unindexed web address. On that basis, it said, the data was not simply open to the public. The company did not dispute the number of files, according to the reports.

The reporting pushes back on that defence. The files themselves carried no password, WIRED and Digital Trends noted, and Fowler found the address inside code on ClarityCheck’s own website. An obscure link is not the same as a protected one, Digital Trends wrote, since a researcher found it and others might too.

TNW has not independently verified the database. The account here rests on Fowler’s research and the reporting by WIRED and Digital Trends, and on ClarityCheck’s statement to WIRED.

A wider pattern

Exposed face data is a recurring problem. In June, the group ShinyHunters published 45GB of Madison Square Garden records that included facial recognition data. Tools that scan faces are spreading, from shop cameras wired to police alerts to services that match a photo to a name. Each new store of face data becomes another target.

The stakes are rising as AI makes it easier to misuse such images. Researchers have warned that generated media has made impersonation and scams simpler to pull off. Analysts have also cautioned that the next privacy breach may not need to leak data in the classic sense to cause harm. A person can change a leaked password; no one can change their face.

Regulators have started to act on large lapses. South Korea fined the retailer Coupang a record sum this year over a major data breach. For the people whose faces sat in ClarityCheck’s store, the worry is simpler: their image was held by a service many of them had never used, and, for a time, anyone who found the link could see it.

Katalyst’s LINK spacecraft will still rendezvous with the telescope but will no longer tug it to a higher orbit.

Forward-looking: Are you the kind of person who hates having blood drawn? In a decision that could potentially make that phobia worse or better, the FDA has approved the use of a robotic device that can draw blood from a patient’s arm – with no human assistance required.

Called Aletta, the robot begins the process by instructing the adult patient on how to position their arm. Once in the correct place, the patient or a supervisor presses a button to begin.

Aletta starts by using near-infrared light and Doppler ultrasound to find a suitable vein in the patient’s arm, distinguishing it from an artery. If none can be found, the process stops.

Once a suitable vein is found, the system will apply a tourniquet, prepare the skin, then insert the needle, dispose of it, and even apply a bandage. The supervising phlebotomist confirms that collection tubes are filled in the correct order and verifies that all tubes are adequately full following the procedure.

“Activating small-talk mode”

If this all sounds concerning, the good news is that the FDA specifies that a supervisor trained in phlebotomy must begin and oversee the procedure.

So, why bother with a machine if medical staff need to be present? The agency writes that one phlebotomist can oversee up to three Aletta devices at the same time, which may help address the current phlebotomist shortage in the US.

“This authorization reflects the FDA’s commitment to advancing innovative medical devices that help meet a critical public health need while maintaining the safety and effectiveness patients deserve,” said Michelle Tarver, M.D., Ph.D., Director of the FDA’s Center for Devices and Radiological Health. “Blood draws are one of the most commonly performed medical procedures in the United States, yet patients may face delays due to a growing shortage of trained phlebotomists.”

Some people aren’t going to be comfortable having a robot push a needle into their veins, of course. But the FDA says Aletta has multiple safeguards. One of these activates if the person moves too much during the procedure, causing the needle to automatically detach and the draw to stop.

Better than a robotic prostate examination machine

Additional onboard sensors can pause the procedure and alert the supervisor if other unsafe conditions are detected.

The FDA added that clinical data showed Aletta achieved blood draw success rates comparable to or higher than those of trained phlebotomists when it attempted a blood draw.

Adeletta’s FDA approval is only the first step. It could be a while before the machines start appearing in medical facilities.

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.

• RCS in Google Messages has stopped working for some users
• The issue mostly seems to be affecting newly set up Samsung phones
• Various fixes have been proposed, but the simplest and weirdest seems the most reliable

RCS (Rich Communication Services) is a worthwhile upgrade to SMS — it brings encryption, read receipts, and more, and it’s accessible via Google Messages on Android. Except right now, it seems not to be working for a number of people.

It’s not clear how widespread the issue is, but various posts on Reddit suggest there are issues with RCS on recent Samsung phones like the Samsung Galaxy Z Fold 8 and Samsung Galaxy S26 Ultra.