Tech
Fetch.ai says LA Hacks project MultiEval used specialist agents to test multi-agent delegation
Fetch.ai said a winning LA Hacks project called MultiEval used its specialist AI agents to test how multi-agent systems should delegate work, with the system running a 50-agent simulation of the US economy overnight.
50 specialized AI Agents ran a simulation of the US economy overnight.
A winning @LAHacks project, MultiEval, used @Fetch_ai‘s specialist agents to test how multi-agent systems should delegate work.
This system autonomously improved another Agent harness and was used to run a…
— Fetch.ai (@Fetch_ai) August 20, 2026
In a post on the company’s official X account, Fetch.ai described MultiEval as a project that used its specialist agents to evaluate other agents and improve an agent harness autonomously. The company said the setup ran through ASI:One and framed the process as “agents evaluating agents.”
How the LA Hacks project was described
According to Fetch.ai, MultiEval was used to test delegation inside multi-agent systems, with specialist agents helping run the benchmark and improve the harness used for the simulation. The company did not provide additional technical details in the post about the exact structure of the evaluation or the full scope of the overnight run.
The post adds to Fetch.ai’s broader LA Hacks presence, but the central claim is limited to the project’s use of specialist agents, the 50-agent simulation and the ASI:One workflow described by the company.
Fetch.ai also has a LA Hacks event page on its official site, which places the project in the context of its hackathon participation and agent-focused materials.
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Tech
Bittensor introduces Gamma Tokens as payment layer
Bittensor has introduced Gamma Tokens as a native payment system for its AI economy, giving users and AI agents a way to pay for subnet services with a Bittensor wallet rather than separate accounts or API keys for each subnet.
The OpenTensor Foundation said Gamma is designed as a prepaid, dollar-valued service credit system for subnet services. In practice, the update is meant to simplify how customers access services across the network while keeping the payment process inside the Bittensor wallet flow.
According to the release, Gamma lets subnets charge for services in dollar-valued credits. Customers fund a deposit by burning Subnet Alpha, then authorize a services spender to charge that deposit as usage accrues. The system also uses a hotkey as an API key, creating a standardized payment layer across subnets.
The foundation described the mechanism as a way to budget usage in dollars while service demand translates into Alpha burn. That structure makes Gamma less about a separate speculative token and more about how subnet services are paid for inside Bittensor’s network design.
For readers following AI-focused crypto projects, the key change is operational rather than promotional: Bittensor is formalizing a payment standard for subnet services, with unified wallet access replacing a more fragmented setup. The announcement did not add further detail on adoption or usage levels at the time of publication.
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Tech
io.net highlights idle GPU supply as AI compute demand grows
io.net is again framing its network around a familiar bottleneck in AI infrastructure: builders need compute, while some GPU capacity remains unused. In an official post on X, the company said startups are burning runway while waiting for compute and argued that idle GPUs can be connected to builders at prices they can afford.
Startups are burning runway waiting for compute.
Meanwhile, GPUs sit underused.
There’s a solution.
Connect that capacity to the builders who need it, at prices they can afford.
At @ionet, we want your next funding round to pay for progress. Not for coffee while you wait on a… pic.twitter.com/Ljzh9LPhTJ
— io.net (@ionet) November 8, 2026
The post positions io.net as a coordination layer for matching spare GPU capacity with AI workloads, with the company saying the goal is to help founders spend funding on product progress rather than waiting on hardware. The message aligns with io.net’s broader pitch in its official blog post on AI infrastructure, which describes the project as an orchestration layer for aggregating and coordinating underutilized GPU capacity.
The update does not introduce a new product launch or a fresh technical rollout, but it does restate the core use case io.net is presenting to the market. That use case centers on making idle compute available to AI builders without requiring them to wait for new capacity to come online.
io.net’s framing also suggests the company is continuing to emphasize affordability and access as the main draw for its network. Rather than presenting itself as a new source of hardware, it is pitching a way to make existing GPU supply more usable for developers who need it now.
For readers following AI-focused crypto projects, the post is best read as a reaffirmation of io.net’s positioning rather than as evidence of a new operational milestone. The company is making a case for coordination over expansion: use the GPUs that already exist, and route them toward builders who can put them to work.
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Tech
NEAR launches Agent Market job board for local services quote agent
NEAR has added a new job posting to its Agent Market for a Local Services Quote Agent, an AI agent designed to contact local providers, collect responses and return side-by-side comparisons of price and availability.
The listing appeared on the official NEAR DevHub channel and was also amplified by the project’s official social accounts. According to the post, the builder who takes on the job would keep ownership of the agent and retain the revenue it generates.
The job description frames the agent as a tool for gathering real quotes from local services rather than a general chatbot. It would reach out to providers, log the replies and organize the results so users can compare options more easily.
Builder-owned agents remain central to the marketplace pitch
NEAR has positioned the Agent Market around the idea that developers can publish agents, set their terms and keep ownership after the job closes. In the official posts, the company said builders keep the agent and can earn when it is hired.
The job board format also suggests a more structured approach to AI agent development, with specific tasks posted for builders to bid on. In this case, the task centers on a practical consumer workflow: collecting quotes from local providers and presenting them in a usable format.
NEAR did not say when the Local Services Quote Agent will be completed or who will take the job. The posting does, however, show that the Agent Market is actively listing new work and pushing the marketplace model as part of its product rollout.
The post NEAR launches Agent Market job board for local services quote agent appeared first on The Cryptocurrency Post.
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