1686 summarised stories about AI Agents, each linking back to the original source. Browse all topics →
Product Hunt 1 month ago 27
Slashy launched Slashy Assistant, an AI-native email client feature that drafts replies in a user's voice and handles follow-ups and inbox triage by connecting to email, calendar, CRM, and meeting notes. The assistant is described as starting to help within five minutes. As a result, users can set plain-English automations and use Slashy inside the app or via iMessage, Slack, or phone to reduce time spent managing email.
GitHub 1 month ago 6
Stonkfly is a fly-connectome simulation that takes public Coinbase BTC-USDC prices and produces fixed buy/sell/hold outputs to place spot orders via Coinbase Advanced.
SiliconANGLE 1 month ago 37
Supio described long-horizon AI agents and a “Firm OS” approach meant to orchestrate multi-day legal workflows across case systems and communications instead of handling single tasks. The company says about two-thirds of case work involves communication with external parties other than the client, driving the need for an agentic operational layer. The result is a shift in law-firm AI from point solutions that improve task efficiency to systems that can document and manage work over time while escalating judgment points to attorneys.
Product Hunt 1 month ago 40
Codex GPU Queue lets users run GPU jobs from multiple Codex tasks through one local Windows queue with a shared broker that starts eligible work automatically. It uses a redacted, read-only CLI to inspect confirmed waiting blockers while leaving uncertain reasons undetermined. As a result, you get centralized job scheduling and clearer dependency visibility across Codex tasks.
Zvi (Don't Worry About the Vase) 1 month ago 16 ● 21 sources
OpenAI’s internally run AI agent efforts led to a reported solution of the Millennium Prize problem on Navier–Stokes and triggered disputes about what was learned, verified, and who should get credit. The effort solved Navier–Stokes in 88 hours, after an internal run that used about 130 billion output tokens for Lean formalization and verification taking an additional 17 hours for Astra. The aftermath shifts the focus to how AI-assisted mathematical research was produced and accounted for, including resource use and contested authorship and data-use claims, rather than to prize eligibility.
Product Hunt 1 month ago 15
Duvi launched this week as a SaaS that lets people build AI agents by describing what they want. The page lists 22 followers. As a result, Duvi positions voice, chat, and actions in one agent that can work across a business’s website and phone/WhatsApp without separate experiences for each channel.
The New Stack 1 month ago 46
The article describes how AI agent deployments can pass CI and automated evals while still producing wrong answers for customers, leaving teams with an “observability gap” where failures can’t be alerted on as “wrong.” The Dynatrace 2026 State of SRE and Platform Engineering report, based on 919 enterprise leaders surveyed globally, found that 77% embed observability in some services but only 40% fully integrate it across all deployments. It argues that teams must add run-time evidence beyond traces—especially retrieval inputs/filters, tool-call/tool-result trajectories, and linked evaluation results—so missing retrieval preconditions and other causes can be reproduced and fixed with regression tests and version-validity checks.
Product Hunt 1 month ago 46
Kanverse GPU Borrow demonstrates a device borrowing GPU compute exposed by another machine using Blink Bridge with user permission. The demo uses a real NVIDIA RTX 3050 Laptop GPU in a live cross-device connection. As a result, an orchestrator (GPT-6 Astra) can request authorization, run one bounded GPU workload, verify output, and then release the GPU.
MarkTechPost 1 month ago 25 ● 2 sources
AWS samples describe long-horizon autonomous coding agents failing when a simple LLM-tool loop runs into context overflow, causing goal loss and poor state retention. Deep Agents offloads any tool response over 20,000 tokens and switches to file-pointer truncation once session context passes 85% of the model window. The article shows that improving agent performance shifts from buying a larger model window to using harness mechanisms like context budgeting/offloading, compaction, todo-state recitation, and cross-session memory that preserve objectives across long runs.
Product Hunt 1 month ago 43
moli was launched as a 40MB headless browser for AI agents. Its size is 40MB. It adds a Rust-built browser option focused on lightweight, fast, high-compatibility use for autonomous AI agent workflows and testing.
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19 October 2026
The biggest thread today isn’t about model capability—it’s about who gets held responsible when AI agents act. Atlassian CEO Mike Cannon-Brookes used his company’s new “Agentic Multiplayer Protocol” to make a blunt point: “the AI did it” can’t be an escape hatch. The idea is to keep accountability with the end user by making agent edits visible in document history, like work from a colleague whose changes you can trace. It’s a practical shift away from the consumer fantasy that an agent can operate in the dark; instead, Atlassian is building a control plane where provenance and auditability are first-class features.
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