Reading

TechCrunch reported on September 28, citing a source with knowledge of the funding, that inference infrastructure provider Modal Labs is nearing a $750 million round led by Accel at a $15.75 billion valuation including the investment. The company declined to comment, and the deal has not been announced.

For comparison, Modal's previous $355 million round announced in May valued it at $4.65 billion — the new figure is more than triple. The company told Reuters in May it had surpassed $300 million in annualized revenue.

The report notes several inference startups are in talks for sharply higher valuations, though margins in the business are thin because compute costs remain high; this round is still in the talking stage, and the size and valuation could change.

Ric Smith, president of products and technology at Okta, said at the company's Oktane event that Okta plans to add intent-based authorization to its agent gateway in 2027, using an agent's context and permitted tools to decide whether a proposed action should proceed.

The agent gateway is an already launched Okta product that sits in the path of actions an AI agent attempts, enforcing authorization decisions at runtime. Smith said Okta had dominantly not been inline before, and moving into the request path lets it do far more decision-making around what it authorizes. The company's acquisition of Permiso Security adds detection capabilities to this push.

Smith acknowledged that scoping permissions narrowly enough to block dangerous actions without blocking legitimate work is an open research problem. Note that the interview was published by SiliconANGLE's theCUBE, a paid media partner for the Oktane event.

Apple plans to launch its first smart home control hub on October 13 local time, alongside refreshed HomePod mini and Apple TV models, Bloomberg's Mark Gurman reported on September 30.

The hub has a roughly 6-inch square screen in desk and wall-mounted versions, recognizes family members and switches to personalized interfaces, and controls devices such as thermostats and door locks. The HomePod mini gets its first hardware update since its 2020 launch, and the Apple TV its first since 2022; both switch to new processors to support the next-generation Siri AI.

According to the report, these products were originally planned for an earlier date but slipped as Siri AI was repeatedly delayed. Apple has not officially confirmed the launch; the date and specifications are as reported.

AI value-accounting startup Ascerta announced on September 30 an $18 million Series A led by Dell Technologies Capital, bringing total funding to $22.9 million.

The company builds tools that account for enterprise AI spending: rather than just counting token consumption, it tracks whether AI applications actually generate revenue or savings. Its platform has three parts — Atlas measures AI ROI, Forge covers engineering teams' coding-agent productivity, and Convoy tracks compute usage.

The article says customers Atos and Wipro improved AI ROI by an average of 47%; those figures are company-reported. The round is modest, but Dell's investment arm leading it signals that AI spend value-accounting is being backed as a distinct category.

Restate, a Berlin-based startup, has raised a $20 million Series A led by Singular with participation from Redpoint Ventures and Capital One Ventures, according to a TechCrunch report on September 30.

Restate builds durable workflow infrastructure: multi-step processes that survive crashes and network interruptions and produce reproducible outcomes. That capability happens to fit AI agents, which run long and take unpredictable paths. The company says it has closed multiple six- and seven-figure customer contracts in recent months, including Replit.

The category leader is Temporal, which announced a $550 million Series E at a $12.55 billion valuation earlier this month. Co-founder Stephen Ewen, a co-creator of Apache Flink, said the new capital will fund a go-to-market team and more engineers. The round's details rest on media reporting so far; no company announcement has been published.

The federal government is funneling local license plate reader data into federal servers through an anti-drug grant program.

According to 404 Media's September 30 report based on public records and court documents, the HIDTA (High Intensity Drug Trafficking Area) program under the White House Office of National Drug Control Policy aggregates data from local police Flock, Axon and other license plate reader cameras onto federal servers, accessible to federal, state and local law enforcement.

Georgia requires cities to sign a memorandum of understanding promising to send data to a HIDTA before operating plate cameras on state rights of way. The report cites the DEA's December 2024 privacy impact assessment as saying this data can then flow into its National License Plate Reader Program.

Jeramie Scott, who runs the Electronic Privacy Information Center's Surveillance Oversight Center, told the outlet that federal agencies without vendor contracts can obtain the data this way. There are 33 HIDTA programs covering all 50 states, and it is unclear how many jurisdictions are actively feeding data in.

GPT-6.1 Sol, without chain-of-thought, closes 80% of the gap between GPT-6 Sol and GPT-6 Astra across 27 tasks (95% CI: 72–87%) — the result of an independent rerun reported by researcher Rauno Arike on LessWrong, and it sits closer to Astra than to GPT-6 Sol on 24 of 27.

The run follows his earlier no-CoT evaluation of Astra: since 6.1 Sol does not support disabling reasoning, he substituted reasoning_effort=low with an immediate-recall system prompt, took k=1 sample per question, and excluded nine tasks that previously failed to separate the older models. Time-horizon estimates moved from 4.0 minutes for GPT-6 Sol to 35 minutes, though the author himself flags that most benchmarks saturate, making those estimates highly uncertain.

His looped-transformer explanation rests on a gpt-6-astra-minor registry path spotted in Azure's playground configuration and community speculation; OpenAI has not confirmed it.

Framework opened pre-orders today for its updated Desktop: the DIY edition with 192GB of memory starts at $6799, while the current 128GB model sells for $3449.

The new machine uses AMD's Ryzen AI Max+ PRO 495, a 16-core Zen 5 chip with 192GB of LPDDR5x-8533 unified memory and Radeon 8065S graphics; the pre-built version with a 2TB NVMe drive is $7449. This unified-memory configuration is mainly aimed at running large models locally.

The first batch is expected to ship in November, so these are pre-orders, not deliveries. Memory cost is the main driver of the jump: adding 64GB nearly doubles the price, a direct cost signal for readers planning local inference hardware.

Some data isn't a row in a table but a set of objects plus the relationships between them — transaction records, a molecule, a road network. Graph neural networks are a family of methods for handling this kind of data: treating objects as nodes and relationships as edges, letting each node repeatedly read its neighbors' information and update itself. This 2021 online tutorial explains the mechanism clearly with interactive demos.

Today, when vendors demo graph models, they often claim the same model can score an entire graph as well as individual nodes or a particular relationship. The tutorial explains why this works: information is passed and aggregated back and forth among nodes, edges, and the whole, and the three types of tasks share one approach. Later architectures mostly swap parts on top of this framework.

It is a tutorial, with no new experiments. If you want to compare which specific architecture performs better on a given dataset, or use it as the basis for some measured result, don't use it to make that judgment.

A Gentle Introduction to Graph Neural Networks (2021) | Next review 2027-09-20

The AI agent Instinct has started pushing product recommendations to users unprompted, and several early users are publicly unhappy.

The feature, called Instinct Selections, went live on September 29. Founder Noah Shinn says the company partners with chefs, designers, architects and travel guides to bring "human taste" to restaurant, trail and home-decor suggestions. What users received were pushes they never asked for: investor Shruti Gandhi was recommended carry-on luggage and sunglasses, and another founder said the agent pitched a coffee flask based on his email and upcoming trips.

Instinct has not said whether the recommendations generate revenue; the feature looks like an entry point for advertising or affiliate income. The company just closed a $1 billion Series C at a $10 billion valuation but has not disclosed user numbers. Against competitors like OpenAI that do not push products, whether this monetization keeps users remains to be seen.