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Built by DeepMind alumni, British AI lab Inherent released Faraday, an AI agent whose ability to replicate scientific papers could be a stepping stone for innovation.

Enterprise vibe coding: the governance framework for shipping AI-generated apps to production Published: August 22, 2026 Category: Enterprise · AI Deployments Reading time: 9 minutes Author: NEXUS AI Team Gartner forecasts that 40% of new enterprise production software will be built using vibe coding techniques by 2028. A 2026 scan of more than 1,400 live vibe-coded applications found that 65% already had a security issue, and 58% shipped with at least one critical vulnerability. Those two numbers describe the same industry moving in opposite directions at once: adoption is outrunning governance. This post covers what a governance framework for enterprise vibe coding actually looks like, the five controls it needs, and where most teams get it wrong. What is enterprise vibe coding? Enterprise vibe coding is the practice of using natural-language prompts to generate application code, then governing that code through mandatory review, access control, and audit before it reaches production, rather than letting it ship straight from a prompt to a live endpoint. The term (coined by Andrej Karpathy in early 2025) originally described a fast, low-friction way for one person to build a prototype. What "enterprise" adds is the governance layer prototyping was never built for: staging environments, encrypted secrets, role-based access, and a record of who approved what. That distinction matters because the adoption curve and the risk curve are not moving together. The governance gap, in three numbers 40% of new enterprise production software will be built using vibe coding techniques by 2028, according to Gartner's May 2025 report "Why Vibe Coding Needs to Be Taken Seriously," as reported by CIO Dive . 65% of vibe-coded production applications had a security issue, in a 2026 scan of more than 1,400 live apps by the API security firm Escape.tech, reported via a Cloud Security Alliance research note . 58% of those same applications shipped with at least one critical vulnerabilit

Android Auto YouTube Limitations: Is It Worth Using In The Car?

OpenAI is calling for California to strengthen SB 53, an AI safety bill that the company previously opposed.

GitHub热门项目 | Sub2API 一站式开源中转服务，让 Claude、Openai 、Gemini、Grok订阅统一接入，支持拼车共享，更高效分摊成本，原生工具无缝使用。 | Stars: 38,723 | 264 stars today | 语言: Go

A new study finds leading AI labs have few publicly documented plans for containing rogue models, raising questions about preparedness as AI systems increasingly demonstrate unexpected and potentially dangerous behavior.

U.S. battery startups pulled in $500 million in grants from the Department of Energy, throwing a lifeline to an industry that was on the ropes after EV incentives were slashed.

Google’s Pixel 11 Pro XL brings snappier cameras and genuinely useful AI features like Rambler, but its iterative upgrades may not be enough to tempt recent Pixel owners.

If you're having trouble with Bluetooth connections on your Pixel phone, this tool is a great place to start your troubleshooting.

Gen AI Academy APAC — Track 1 (AI Agents with Gemini, ADK, and Cloud Run) Why I built this I lead a customer service team of 25 agents at Amazon, handling both buyer-side and marketplace seller support. A big part of my job is reviewing escalated cases — calls or chats where a customer asked for a supervisor — and figuring out why they escalated in the first place. Was it a policy gap? A training issue? A system limitation nobody flagged? Right now, that review is manual. Every escalation gets read, tagged, and turned into a coaching note by a human — usually me, or one of my leads. It works, but it doesn't scale well, and patterns across dozens of cases are easy to miss when you're reviewing them one at a time between everything else on your plate. So for Track 1 of the Gen AI Academy APAC program, I built an agent that does the first pass of this analysis automatically: read an escalation summary, classify the root cause against a standard taxonomy, flag whether it looks like a repeat pattern, and draft a coaching note — the same way I would, just faster and more consistently. What it does The agent takes a case summary like this: Customer requested a refund for a damaged item outside the standard return window. Agent denied it citing policy; customer says a rep last month approved a similar exception for someone else. And returns a structured analysis: { "root_cause_category" : "policy_misapplication" , "severity" : "medium" , "is_likely_repeat_pattern" : true , "pattern_reasoning" : "Inconsistent policy application across agents suggests a training or documentation gap rather than an isolated error." , "coaching_note" : "..." } It's built on Google's Agent Development Kit (ADK) with Gemini as the underlying model, and deployed as a live service on Cloud Run . The agent has one tool — a lookup function for the standard root-cause taxonomy — which keeps the categories consistent and easy to update without touching the core prompt. For batch review, I also built a

The neon flying squid can fly in formation. The shoal of about 100 squid rose unexpectedly from a patch of the Pacific Ocean around 370 miles from Tokyo and glided near the boat for about 30 metres. The astonished researchers were the first to capture photographs of such a thing, which looked like the early stages of an alien invasion. They were probably neon flying squid ( Ommastrephes bartramii ), the subsequent study states , a species that is part of a 20-strong flying squid family that was known to leap from the water but, until then, was only rumoured to also be able to glide above it...

Two years after the U.S. Department of Justice alleged that TikTok violated the Children’s Online Privacy Protection Act, it has reached a $400 million settlement.

The private Android-based OS will expand beyond Pixels next year.

Walmart will soon allow you to pay for your items with Google Pay or Apple Pay. In an announcement on Friday, Walmart says it's going to bring tap-to-pay capabilities to "select" Walmart and Sam's Club locations starting August 24th, before rolling out support to all US stores by the end of 2026 and gas stations […]

This sort of research is both exciting and terrifying: The two models in question were told to generate complete genomes for a viable bacteriophage—a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside. Using an existing bacteriophage as an example—ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria—the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising. The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge...

The security review is being performed by the Idaho National Laboratory, and the research is being funded by a company -- or a group of companies -- in the electric and autonomous vehicle industries.

Are pigs flying? Walmart has finally caved on its refusal to support Apple Pay and Google Pay.

I recently built WordScrambler, a free tool for unscrambling letters and solving anagrams, mostly out of frustration with existing tools being cluttered with ads or requiring sign-up just to see a result. Here's a quick look at the core technique behind how it works. The problem Given a jumbled set of letters (say, ucim), find every valid dictionary word that can be formed from some or all of those letters. The naive approach, generating every permutation and checking each against a dictionary, gets slow fast. A 7-letter input has 5,040 permutations; a 12-letter input has nearly 480 million. That's not viable for instant results. The signature trick The key insight: two words are anagrams of each other if and only if their letters, sorted alphabetically, produce the same string. For example: "listen" -> sorted -> "eilnst" "silent" -> sorted -> "eilnst" Both hash to the same signature. So instead of generating permutations, you can: Precompute a signature for every word in your dictionary and group words by signature. For a given input, generate the signature of the input (and its relevant sub-combinations, for partial-length matches). Look up matching signatures in a hash map, an O(1) lookup instead of a brute-force search. This turns "find every valid word from these letters" into a fast lookup problem rather than a combinatorial one, which is what makes results feel instant even against a large dictionary (WordScrambler checks against roughly 246,000 words). Handling partial-length matches Most real unscrambling needs go beyond "use every letter", people want every valid word of any length using a subset of the given letters. That means generating signatures for all relevant letter subsets (not full permutations, just subsets, which is a much smaller set) and checking each against the dictionary map. Try it You can play with the live version here: wordscrambler.online — it also shows word definitions and Scrabble/Words With Friends point values alongside each resu

Cloudflare has recently detailed how it is using AI to transform internal engineering standards from passive documentation into an actively enforced control system across the software development lifecycle. By Craig Risi

Full walkthrough: https://www.youtube.com/watch?v=vFuFA3ByCs0 LeetCode 3116 — Kth Smallest Amount With Single Denomination Combination. Here’s the trick everyone misses: Brute force (generate all multiples, pick k-th) fails because k can reach 2×10⁹. The real approach: Binary search the answer X Count valid amounts ≤ X using inclusion-exclusion Odd subsets add, even subtract (bitmask over coins) LCM via GCD, break when LCM > X O(n · 2ⁿ · log(k·M)) — passes cleanly. The 26% acceptance rate makes this look harder than it is. Once you see the count(X) monotonic trick, it clicks.