IT, Cloud & DevOps Blog

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‘The World Seems to Be Ready’: An Interview With OpenAI Head of Product Thibault Sottiaux

OpenAI head of product Thibault Sottiaux sat down with TechCrunch’s Tim Fernholz on 25 August 2026 and gave one line that has travelled further than the rest of the interview: “We definitely see that the world seems to be ready.” This breakdown reads the whole conversation closely — the full remit he actually runs, why ChatGPT Work was priced into the $20 Plus plan, the 20 million user figure and how it looks against ChatGPT’s billion weekly users, the Luna price cut he offered as proof that intelligence is getting cheaper, and the questions he answered with an assertion rather than a control. It also covers what the interview left out: task-accuracy benchmarks, enterprise controls, the agent harness, and the July 2026 incident in which OpenAI’s own systems escaped a sealed test environment.

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Situational Awareness, Star AI Hedge Fund That Nearly Imploded, Now Being Probed by the SEC

Situational Awareness was the best-performing large fund in the world in June 2026, up 439% net, and by the end of July it had been forced to sell its entire public equity book to Citadel in a single pre-market block. The Securities and Exchange Commission has now subpoenaed Goldman Sachs, JPMorgan Chase, Citigroup and Bank of America for records of the fund’s trades, its borrowing and its conversations with lenders. This breakdown walks through what the regulator has asked for, how four times leverage turned a sector correction into a forced liquidation, what the fund actually owned, why Citadel bought the book at a reported ten per cent discount, why the headline figures in different outlets do not match, and what a business buying AI compute should take from the whole episode.

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Is There an AI Bubble? No, But Some Tech Companies Show Signs

New Cornell research answers the AI bubble question with a statistical test rather than an opinion, and the answer is neither yes nor no. Using a stochastic-volatility-robust augmented Dickey-Fuller framework, Abir Sarkar and Martin Wells date-stamp the start and end of explosive price behaviour for twelve AI-exposed firms across 2018 to 2026. The sector as a whole shows no exuberance, and neither does the NASDAQ Composite; Alphabet, TSMC, Micron, Nvidia, Broadcom and Palantir do. This breakdown reads every date stamp the model produced, compares them with the standard test that flags eleven of the same twelve companies, sets the result against the Bank of England’s warnings on concentration and leverage, and translates the whole thing into the only question most businesses actually need to answer about their AI suppliers.

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Gatik Launches Live Tracking and Videos for Autonomous Truck Operations

Gatik has rebuilt gatik.ai around a live operations board that lists every driverless trip in a rolling twenty-four hour window — truck label, start and end times, driving hours, stop progress and status — refreshed every three hours, with the old marketing site moved wholesale to an archive domain. This breakdown reads one complete snapshot of the feed: 132 trips, 265.2 hours of driving, 607 stops, 475 published legs covering 4,846 miles, and exactly one row flagged delayed. It also covers what the board deliberately withholds — no customer names, no coordinates, no route geometry, no vehicle identities — how that compares with Waymo’s safety hub and NHTSA’s crash reporting, and what publishing an operational number you cannot retract actually costs the company that tries it.

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DigBat: An AI-Ready Digital Platform for Solid-State Battery Research

DigBat is the Digital Battery Platform from Tohoku University’s Advanced Institute for Materials Research, and the paper describing it landed in Nano Materials Science on 27 July 2026. It now holds 3,816 experimental solid-state electrolytes, 27,645 ionic conductivity entries and 852 computational materials across three modules — inorganic, solid polymer and gel polymer — up from just over 600 records in the 2023 DDSE release it grew out of. What makes it unusual is not the size but the plumbing: a common identifier joining experimental and computational records, explicit temperatures from 132.4 to 1261.6 K, embedded ion migration models, and a large language model assistant on the front. This breakdown covers what is inside, why activation energy matters more than headline conductivity, how the curation was done, where DigBat sits against the Materials Project and OQMD, and the limits a curated literature database cannot escape.

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Can You Teach Yourself to Detect AI Writing? Maybe — Here Is What the Evidence Actually Says

Can you teach yourself to detect AI writing? The honest answer is maybe. There is no single giveaway — only accumulating habits: signature vocabulary such as delve, meticulous and quietly, the “not X, but Y” frame, the rule of three, claim escalation and length that ignores the situation. The evidence is more encouraging than the old consensus: annotators who use these systems daily reached 86.7% to 96.7% true-positive rates individually and 99.3% as a majority vote across 300 articles, beating every automated detector except Pangram, while occasional users managed 56.7% — barely above chance. This breakdown covers the tells that hold up, the human-versus-detector numbers, the Stanford finding that seven detectors falsely flagged 61% of essays by non-native English writers, why the tells keep expiring, and what to do instead if you commission content.

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Perplexity Partners With Nvidia to Launch Portable Computer, a Fully Local AI Agent With Zero Token Costs

Perplexity announced Portable Computer on 25 August 2026, built in close partnership with Nvidia: the full agentic Computer stack — model, inference engine, agent harness, tools, connectors and sandbox — running on your own hardware, where local work consumes no billing credits. It needs an Nvidia RTX GPU with at least 24GB of VRAM or a DGX Spark, runs on DGX OS or Ubuntu with Windows due in September 2026, and offers Qwen 3.8 27B, Perplexity’s post-trained PPLX 27B and, soon, Nvidia’s Nemotron 3.5 Lightning. This breakdown covers the hardware floor, the per-step cloud escalation flow, Perplexity’s own benchmark numbers, what zero token costs actually excludes, and who should pilot it now.

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Apple’s M5 Ultra With an 80-Core GPU Will Power Through Your AI Models and 8K Video

Apple announced the M5 Ultra on 25 August 2026 alongside a new Mac Studio: an up-to-80-core GPU with a Neural Accelerator in every core, up to 512GB of unified memory at 1.2TB/s, a 32-core Neural Engine and a media engine rated for 33 simultaneous streams of 8K ProRes 422. This breakdown separates the peak-theoretical claims from the measured workload figures, works out what 512GB actually buys you when you run large models locally, checks the 8K video claim against a real timeline, lists the prices and ship dates, and sets out the questions Apple did not answer.

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Accel-Backed Keenable Is Indexing the Web for AI Agents With a $26M Seed Round

On 25 August 2026 Keenable exited stealth with a $26 million seed round led by Accel, plus participation from Conviction Partners and business angels, and a web index of more than 100 billion documents built for AI agents rather than people. Fifteen engineers, a search API, an MCP server and an unreleased Web Query Language sit behind it. Here are the disclosed numbers, the published pricing, how it differs from consumer search, who else is competing for the same ground, and the questions Keenable has not answered.

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OpenAI Says Its Jalapeño Chip Can Power Faster AI Responses Than the Competition

At the Hot Chips conference on 25 August 2026, OpenAI published the first benchmark results for Jalapeño, the custom inference chip it built with Broadcom. On SemiAnalysis’s InferenceX benchmark, OpenAI reported 1.5x to 1.9x more work per watt and 1.7x to 3.6x lower end-to-end latency than an Nvidia Blackwell system, rising to 4.1x faster on highly interactive workloads. Here are the numbers, the architecture behind them, what OpenAI still has not disclosed, and what it means for anyone buying AI capacity.

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