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Only Thu, September 24, 2026 · All dates →

A million agents is a distributed system problem

A Hacker News discussion argues that agents at scale are a distributed systems problem, so durable state, not the agent, should persist. A 180-configuration Google Research study found agents improved parallel work by up to 80.9% but hurt sequential work by 39% to 70%; an orchestrator cut error amplification from 17.2x to 4.4x, and in Silo-Bench the hardest tasks hit zero success at 50 agents.

Hacker News ·

Harness as Important as Model for Computer Use

An HN discussion argues that in computer-use benchmarks, model performance, cost, and speed depend as much on the software harness as on the model itself. It calls for evaluating models under the same harness and focusing on browser-agent capability.

Hacker News ·

Google Photos AI virtual closet launches on iOS and Android

Google Photos' AI-powered virtual closet is now available to iOS and Android users in the U.S., Brazil, and India, after first rolling out to Android in June. It uses AI to catalog items from users' photos and let them filter and mix outfits, and Google says clothing data isn't shared with third parties.

TechCrunch ·

ElevenLabs reportedly valued at $22B

Voice AI company ElevenLabs is reportedly valued at $22 billion by investors, with annual recurring revenue pacing at about $600 million. Its voice models power customer service for Klarna, Deutsche Telekom, Cisco, and Adobe, and CEO Staniszewski said businesses should disclose when customers talk to an AI.

TechCrunch ·

UkisAI Releases Swift Family of Efficient Reasoning Models

UkisAI launched the Swift family of reasoning LLMs built on Qwen, cutting thinking tokens by penalizing pathological overthinking and restoring accuracy via RL. Swift Flash Next reduces thinking tokens 63.4% with 1.8x speedup; Swift1.5 27B cuts 58.5% with slightly higher scores; Swift Bonsai 2 cuts 39.8%. API, demos, and quants are provided.

r/LocalLLaMA ·

AI performance costs falling faster than past tech

Epoch AI estimates AI inference prices at fixed performance fall about 13x per year; MIT puts annual algorithmic progress at about 3x after stripping out hardware gains and competition. Yet today's best models aren't necessarily cheaper—reasoning models can cost more due to higher compute per task. Real-world selection also depends on quality, speed, and error rate.

The Decoder ·

Google tests Gemini calling businesses for users

Google launched "Call for Me," letting Gemini place calls to businesses on users' behalf. It debuts for U.S. Pixel 11 owners with a Gemini subscription and the beta Phone app. Gemini can share approved personal information and handle tasks like checking stock, booking restaurants, or rescheduling appointments, while users follow a live transcript and can take over anytime.

TechCrunch ·

AI chipset market to accelerate through 2035

The artificial intelligence chipset market is expected to accelerate through 2035, driven by hyperscale data center demand, according to a report. This trend indicates continued expansion of AI infrastructure investment.

IndexBox ·

ALEC warns against CON-style AI health rules

ALEC argued against Certificate of Need-style regulation of health-care AI. It noted CON laws require facilities to prove necessity to state governments, cost tens of thousands of dollars and years to complete, and ultimately raised costs and limited supply. It warned that preemptively restricting AI over spending fears would repeat those mistakes.

alec.org ·

Lovable's annualized revenue tops $600M

Vibe-coding platform Lovable said its annual run-rate revenue has passed $600 million, up from about $500 million in June. Its co-founder said two-thirds of Fortune 500 companies now use the product and apps built on it draw nearly a billion monthly views; the startup has raised over $700 million in two rounds eight months apart at a $13.3 billion valuation.

TechCrunch ·
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