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Summaries are not the original full articles. ## I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents DevFeed: [I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents]() Original publisher: [Read original article]() Author: Parth Illendula, Garik Livingston, Zain Shamsi, Ravi Srinivasan, Alex Liu Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security]() Topics: [prompt injection](), [LLM security](), [Adversarial attacks](), [Chain-of-thought](), [llm-reasoning](), [AI Agent]() Tags: [abstract](), [agent](), [agentic](), [agentic-systems](), [agents](), [all](), [also](), [arxiv](), [asr](), [attack](), [attack-surface](), [attacks](), [chain-of-thought](), [class](), [components](), [containerized](), [cs-ai](), [cs-cr](), [cyber](), [i](), [reasoning](), [thought]() ### AI overview The study reports that thought injection can succeed when untrusted text imitates a reasoning model's own deliberation, with results varying by target model. In tests of open-weight models, the effects of payload style and thinking tags were model-specific. In a containerized agentic cyber range, indirect prompts styled as the target's reasoning traces reached attack success rates of up to 50% on objectives including reverse-shell exfiltration and repository backdooring; the same objectives phrased in a standard user voice never succeeded. Thinking tags reduced disclosure of the injection, but stripping tags was an unreliable defense. ### Source excerpt arXiv:2611.08811v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) are one of the key components of current agentic systems. LRMs augment their output with an explicit chain-of-thought reasoning stage, introducing a new attack surface for thought injection, in which untrusted text resembling the model's own deliberation is fed back to the model. We present a two-part study of this attack, performed against the prevailing large, production class open-weight models (e.g., GLM-5.3, MiniMax-M2.5, Qwen3.8-Flash-Next). We first evaluate the effectiveness of static thought-injection prompts under different styles as well as tagged and untagged conditions and find that the effects of thinking tags and payload styles are all properties of the specific target model. In the second study, we exploit this model-specificity in a containerized agentic cyber range, using indirect injection prompts styled in the form of the target model's own reasoning traces to achieve attack success rates (ASR) up to 50% on operational objectives such as reverse-shell exfiltration and repository backdooring. We also find that the same malicious objective in standard prompt injection user voice never succeeds, and thinking tags suppress the agent's tendency to disclose the injection. Our results show that imitating a reasoning model's own thought style is effective for this style of attack, and that tag stripping is an unreliable defense. ## 17 Simple Raspberry Pi Tricks That Save You Time DevFeed: [17 Simple Raspberry Pi Tricks That Save You Time]() Original publisher: [Read original article]() Author: Usman Qamar Published: 2026-11-07T05:00:00Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Raspberry Pi](), [Tutorial]() Tags: [17](), [but](), [can](), [easier](), [everyday](), [i](), [inspiration](), [little](), [make](), [my](), [over](), [pi](), [pretty](), [raspberry](), [raspberry-pi](), [save](), [simple](), [small](), [tasks](), [time](), [tricks]() ### AI overview The article presents Raspberry Pi time savers across the terminal, Raspberry Pi OS, and recurring setup tasks. Terminal tips include TAB completion, Ctrl+R history search, `sudo !!` to rerun the last command with elevated privileges, piping output through `grep`, and using `watch` for repeated monitoring. On the desktop, it recommends configuring shortcuts and pinned apps, bookmarking web interfaces, using Raspberry Pi Imager customization to reuse credentials and connectivity settings, and accessing another computer's files through the file manager's SFTP address. For repeated work, it suggests aliases for short commands and scripts for multi-step tasks, adding script folders to PATH, predictable hostnames and SSH aliases, and SSH keys for password-free connections and automation. It also recommends saving SD card images for projects that may need restoring, while noting image creation takes time. ### Source excerpt Over the years, I've picked up a lot of little Raspberry Pi tricks to make everyday tasks easier. Most of them are pretty simple, but that's what makes them so useful. I've put together 17 of my favorites, and I'm sure you'll find a few that save you time too. Small Raspberry Pi tricks can... ## The AI boom's missing balance-sheet question: What has actually been verified? DevFeed: [The AI boom's missing balance-sheet question: What has actually been verified?]() Original publisher: [Read original article]() Author: Neil P. Osnato Published: 2026-11-06T15:00:36Z Content type: opinion Language: en Sources: [SiliconANGLE]() Topics: [AI Infrastructure](), [Finance](), [decision-making](), [clean energy data centers](), [Artificial Intelligence]() Tags: [ai](), [ai-boom](), [ai-infrastructure](), [artificial-intelligence](), [balance](), [balance-sheet](), [capacity](), [exchange](), [faster](), [generative-ai](), [i](), [infrastructure](), [intelligence](), [me](), [neil-p-osnato](), [new-york](), [news](), [one](), [persistent-analytics-group](), [question](), [siliconangle](), [stock](), [the-latest](), [top-story-4](), [top-story-6](), [trading](), [transaction](), [value]() ### AI overview AI infrastructure commitments can be valid and still rest on assumptions that may not hold over the investment's life. The article distinguishes contracted compute demand from productive utilization and says announcements, financing, power agreements and GPU orders do not by themselves prove capacity will be deliverable and used as planned. It proposes tracking a sequence from requested and evidenced load through deliverable, energized and utilized capacity to sustained demand, with supporting records for each claim. Guarantees and other contractual protections can mitigate losses but do not ensure the asset remains useful or remove counterparty and dependency risks. The recommended control is ongoing revalidation: document the representations, assumptions, evidence, dependencies, downside exposure and changes that should trigger a reassessment before further commitments. The author frames this as investment discipline, not a prediction that AI infrastructure will fail. ### Source excerpt Artificial intelligence infrastructure commitments are growing faster than the evidence connecting contracted capacity to sustained economic value. I spent 13 years on the floor of the New York Stock Exchange. One lesson stayed with me long after the trading jackets disappeared: A transaction could be executed, documented and perfectly valid while the assumptions that made [...] The post The AI boom's missing balance-sheet question: What has actually been verified? appeared first on SiliconANGLE. ## What I Learned Mapping Public Record Links Across 56 Korean Local Councils DevFeed: [What I Learned Mapping Public Record Links Across 56 Korean Local Councils]() Original publisher: [Read original article]() Author: Hoyeop Jung Published: 2026-11-06T08:20:43Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Documentation](), [Digital Public Good](), [Local AI]() Tags: [2026](), [about](), [accessible](), [ai](), [all](), [also](), [archive](), [bill](), [bom](), [civic-tech](), [csv](), [data-engineering](), [files](), [github](), [i](), [jalhana](), [json](), [korean](), [link](), [links](), [local](), [open](), [open-data](), [programing](), [public](), [public-records](), [south-korea](), [with]() ### AI overview The directory maps four official record entry points for each of 56 South Korean local councils: council websites, meeting minutes, bills and motions, and member directories. Its 224 links are published as CSV and JSON, with source URLs, check dates, intended scope, and separate labels for observed official navigation, checked HTTP responses, and user-confirmed destinations. Those checks describe evidence at a particular time, not a guarantee that links remain valid. The directory offers repeatable starting points but does not collect all council records or redistribute minutes, bills, or attachments; researchers still need to examine the official systems and judge how records relate. The CSV includes a UTF-8 BOM for Excel; pandas users should read it with `encoding="utf-8-sig"`. ### Source excerpt Lessons from mapping official record links across 56 South Korean local councils, with open CSV and JSON files and notes on how each link was checked. ## Why I Ditched Google Drive for Syncthing and Tailscale DevFeed: [Why I Ditched Google Drive for Syncthing and Tailscale]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-11-06T08:05:45Z Content type: article Language: en Sources: [HackerNoon]() Topics: [tailscale](), [Synology]() Tags: [about](), [actions](), [all](), [allows](), [also](), [announcement](), [app](), [apply](), [apps](), [database](), [drive](), [google](), [google-drive](), [hackernoon-top-story](), [i](), [keepass-xc](), [password](), [privacy](), [private](), [secure-file-sync](), [sync](), [synchthing](), [syncthing](), [synology](), [tailscale]() ### AI overview The author replaced Google Drive for a KeePass database after KeePassium sometimes kept pointing to an old file when KeePassXC replaced it during saves. Syncthing now syncs the `.kdbx` file among a laptop, phone, iPad and always-on Synology NAS, with Tailscale providing stable private addresses. On the NAS and Mac, the author disables Syncthing discovery, relaying and NAT traversal; the NAS keeps recent file versions. The iOS app Sync Anything cannot disable discovery or relaying, and iOS background limits mean sync may lag, so the author opens it before KeePassium after password changes. Offline edits can create conflict copies, which KeePassXC can merge. The NAS-centered setup stops syncing when the NAS is down, and its version history is not backed up; current copies remain on the other devices. The author currently syncs only the password database and leaves other files on Google Drive. ### Source excerpt Tired of Google Drive sync issues? Here's how I built a private password database sync system using Syncthing, Tailscale, and Synology. ## Notion vs. OneNote: Which note-taking app should you use? \[2026\] DevFeed: [Notion vs. OneNote: Which note-taking app should you use? \[2026\]]() Original publisher: [Read original article]() Author: Ryan Kane Published: 2026-11-06T05:00:00Z Content type: comparison Language: en Sources: [Zapier]() Topics: [Notion](), [Tool](), [AI Chat](), [workflow automation]() Tags: [2026](), [about](), [all](), [app](), [app-picks](), [apps](), [aruba](), [book](), [can](), [chat](), [help](), [i](), [it-s](), [kind](), [life](), [my](), [note](), [note-taking](), [notion](), [pakistan](), [vs]() ### AI overview OneNote is the simpler choice for capturing notes in varied formats; Notion suits people who want notes integrated with structured productivity systems and team collaboration. OneNote supports freeform pages, handwriting, drawing, audio and video recording, equations, and locally stored notebooks that sync when online. It also lets users password-protect notebook sections on desktop and iOS. Notion organizes notes in searchable databases with filtered views and linked pages, and supports real-time collaboration with comments and mentions. Its offline use requires downloading pages in advance, while paid plans automatically download recent and favorite pages. Both offer free plans, but with different limits: OneNote shares 5 GB of storage across Microsoft apps; Notion permits unlimited individual content if each uploaded file is under 5 MB. Notion's AI can create and update workspace databases and search internal content, with full access on Business; Copilot in OneNote is included with eligible Microsoft 365 plans and offers summaries, to-do lists, and study tools. The author found Copilot Notebooks failed to ground an answer in an uploaded source document during testing. Choose by ### Source excerpt I can't think of many apps that inspire the kind of devotion that Notion does. Its fans organize meetups all around the world to chat about productivity systems, from Aruba to Poland to Pakistan. Heck, you can buy Notion onesies for your baby. My theory on Notion's popularity is this: because you can use it to organize pretty much anything, it's become the software equivalent of a self-help book. Whether you're struggling with school, work, or your personal life, there's a Notion template design ## Stop Sending Tall PR Stacks DevFeed: [Stop Sending Tall PR Stacks]() Original publisher: [Read original article]() Author: Zac Sweers Published: 2026-11-06T04:30:16Z Content type: opinion Language: en Sources: [Zac Sweers]() Topics: [Pull Request](), [Code review]() Tags: [2](), [actions](), [all](), [approach](), [commit](), [commits](), [i](), [it-s](), [large](), [logical](), [medium](), [possible](), [pr](), [review](), [rules](), [simple](), [small](), [this](), [works](), [write]() ### AI overview The author argues that small, logical commits reviewed one at a time can keep even large pull requests readable, despite GitHub's file-tab workflow grouping files alphabetically. Stacked pull requests are useful in some cases, but very tall stacks multiply the CI and review workload; the author says stacks should be exceptional and suggests a maximum height of about five for substantial changes. The limit is presented as an opinion, not a measured rule. ### Source excerpt I've long held the belief that it's always possible to write perfectly readable medium, even large sized PRs that are super reviewable if you abide by the following two simple rules: Use small, logical commits Review commit by commit This approach works phenomenally well. Small commits ## Well, That Wasn't Fun: October 16 '26 DevFeed: [Well, That Wasn't Fun: October 16 '26]() Original publisher: [Read original article]() Author: Daniel Botha Published: 2026-11-06T00:00:00Z Content type: news Language: en Sources: [Fly]() Topics: [fly.io](), [virtual machines](), [virtualization](), [TCP/IP](), [Cryptography](), [payload](), [network-intelligence]() Tags: [2026](), [about](), [based](), [bug](), [but](), [can](), [card](), [cdn](), [chaos](), [checksums](), [close-to-users](), [code](), [corruption](), [cpu](), [customer](), [cve](), [deploy-app-servers](), [docker](), [elixir](), [example](), [exploit](), [field](), [fleet](), [fly](), [fly-io](), [fun](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [networking](), [october](), [postgresql-clusters](), [servers](), [the-fly-blog]() ### AI overview Fly.io patched and rebooted its fleet after CVE-2026-99999 exposed a flaw in Linux IPv4 checksum-offload validation for virtual machine traffic. By redirecting checksum writes, an attacker could alter two trusted IPv4 header bytes after routing and firewall checks, enabling a firewall bypass or a 32-byte out-of-bounds read that might expose host or neighboring VM data. Fly.io says it has no evidence customer data was exposed, but advises customers who ran there October 15-17 to rotate credentials sent over unencrypted protocols, including plaintext traffic over 6PN. The 10-hour rollout required staggered host reboots to limit disruption to Managed Postgres clusters; some machines remained down after hardware and reinstall problems, with no machines or customer data lost. Fly.io also revised incident communications so executives own the communication plan. Its practical security guidance includes encrypting internal traffic, using scoped and expiring agent credentials, limiting shared exposure across trust boundaries, treating agent inputs as hostile, and rehearsing credential rotation. ### Source excerpt October 16, 2026 began like any other day at Fly.io (meaning, we were anticipating only a medium amount of chaos). By that evening, we had patched and rebooted our entire fleet in response to a Linux kernel bug. The full rollout took 10 hours, but the emotional damage persists. CVE-2026-99999 The rest of this won't make much sense without a clear read on the bug itself, so: the Linux kernel CVE team and pwn.ai announced CVE-2026-99999 on the morning of the 15th. We heard about it that night, and started working on mitigations. IP sleight of hand Protocols like TCP and UDP include checksums to detect corruption on the way across the internet. Calculating one means reading the whole message, which adds up at high speeds. Linux avoids that with checksum offload: instead of calculating the checksum immediately, the kernel stores metadata saying where the checksum should read from and write to. If the checksum turns out not to be needed, the kernel never spends the CPU on it. Not needed, for example, when sending to localhost, or when the network card can add its own checksums. Checksum offload works the same way for virtual machines. The virtual network card accepts checksum metadata and passes it to the host. On the host side, the packet is treated like any other offloaded packet, using the metadata the guest provided. The offload code has checks meant to reject bad metadata. For IPv4, they were wrong. To exploit that, an attacker needs to do three things: point the checksum at a different header field, pick a message that produces the checksum they want, and force the host to calculate the checksum in software. The pwn.ai post goes into detail on pulling that off. The end result: the host receives the packet, parses it, decides where to send it, then applies the checksum, in the wrong place. The attacker now controls two bytes of an IPv4 header the host has already trusted. Based on network activity during the rollout, we believe it's unlikely that any of our customer ## Bring a tent, I've got the coffee: Lessons from it-sa DevFeed: [Bring a tent, I've got the coffee: Lessons from it-sa]() Original publisher: [Read original article]() Author: Thorsten Rosendahl Published: 2026-11-05T19:00:00Z Content type: opinion Language: en Sources: [Cisco Talos Blog]() Topics: [Cybersecurity](), [ai security](), [LLM security](), [Cryptography]() Tags: [1-3](), [1201](), [2](), [2025](), [2026](), [22](), [about](), [act](), [apprenticeship](), [but](), [coffee](), [germany](), [i](), [initiative](), [it](), [it-security](), [join](), [program](), [security](), [tent](), [threat-source-newsletter]() ### AI overview The author argues Germany needs an IT Security apprenticeship route so younger people can enter cybersecurity before university age. The excerpt counts nearly 30 cyber bachelor programs but none of the existing IT Specialist apprenticeship specializations devoted to security; an initiative is seeking to add one. It contrasts the roughly 80% university and 20% apprenticeship split in IT with an estimated 800-900 degree graduates annually against demand above 100,000, while noting the graduate figures are the author's estimates and official statistics are unavailable. The newsletter also reports Talos's discovery of VOLTA, a Windows infostealer that sends stolen credentials, sessions and wallet data to an LLM for monetization ranking, with multi-model fallback. Suggested defenses include monitoring Chrome launches with `--headless`, protecting credentials in hardware-backed stores, revoking potentially exposed tokens, changing passwords, and migrating crypto funds if a seed phrase was stored in plaintext. ### Source excerpt Thorsten may have had to camp out in Nuremberg to attend it-sa, but the real story is the initiative to add a formal IT Security apprenticeship program in Germany for the thousands of young hackers ready to join our side. ## Who Are You? DevFeed: [Who Are You?]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-11-05T13:29:28Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [AI Models](), [human centric design]() Tags: [about](), [agents](), [ai](), [ai-agents](), [ai-recommendations](), [ai-slop](), [all](), [amplifier](), [amplify](), [applications](), [approach](), [co-intelligence](), [event](), [i](), [intelligence](), [keynote](), [nyc](), [partner](), [replacement](), [using]() ### AI overview The author argues that AI amplifies existing expertise and judgment, but can magnify weak understanding when people mistake confident output or fast building for progress. He illustrates this with a chatbot built without user interviews that nutritionists rejected because scheduling was part of their relationship-based work, and with his own writing, where AI editing erased his voice and drove readers away. He now uses tone and authenticity guidance while keeping decisions and review in his hands. His practical advice is to begin strategy, drafts, customer conversations, and decisions without AI, then use it to challenge the work. He emphasizes customer understanding, direction, and value creation over tool mastery or rapid software demos. ### Source excerpt I was in NYC for an event about a year ago now, and Ethan Mollick gave the opening keynote, where he explored humans using AI as a co-intelligence partner, not a replacement, as many do now. ## How To Easily Install Ubuntu Desktop 26.10 on Raspberry Pi DevFeed: [How To Easily Install Ubuntu Desktop 26.10 on Raspberry Pi]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-11-05T02:13:57Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Ubuntu](), [Raspberry Pi](), [Tutorial]() Tags: [alternatives](), [but](), [computer](), [desktop](), [easily](), [experience](), [great](), [how-to](), [how-to-tutorials](), [i](), [install](), [it-s](), [know](), [like](), [more](), [my](), [pi](), [raspberry](), [raspberry-pi](), [ubuntu](), [ubuntu-desktop]() ### AI overview The tutorial describes installing Ubuntu Desktop on a Raspberry Pi with Raspberry Pi Imager, then completing the first boot wizard. It recommends a Raspberry Pi 5, 500, 4, or 400 with at least 4GB RAM, a 16GB minimum SD card (32GB recommended), adequate power, and a keyboard and mouse; it suggests Ubuntu Server or Core for less capable models. After flashing the image from "Other general-purpose OS," users set language, keyboard, network, account, and timezone. Ubuntu provides a desktop dock, settings, basic apps, and an app store; Firefox may feel slow, and some applications may not support Pi hardware. Apps can also be installed with APT. The text identifies the system as Ubuntu Desktop 26.04, despite the title specifying 26.10. ### Source excerpt I use Raspberry Pi OS for most of my tutorials because it's simple and reliable. But when I want my Raspberry Pi to feel more like a regular desktop computer, Ubuntu Desktop is one of my favorite alternatives. If you know Ubuntu on a PC, this is a great way to get the same experience... ## AMD Cannot Catch Nvidia, But It Sure Can Make Some Money DevFeed: [AMD Cannot Catch Nvidia, But It Sure Can Make Some Money]() Original publisher: [Read original article]() Author: Timothy Prickett Morgan Published: 2026-11-04T20:31:15Z Content type: opinion Language: en Sources: [The Next Platform: In-depth coverage of high end computing]() Topics: [Finance](), [Nvidia](), [rocm]() Tags: [2021](), [2023](), [2025](), [2026](), [about](), [academic](), [accelerators](), [acquisition](), [added](), [agentic](), [amd](), [but](), [can](), [competition](), [compute](), [cpu](), [epyc](), [function](), [gpu](), [helios](), [i](), [instinct](), [make](), [money](), [nvidia](), [requires]() ### AI overview AMD's datacenter business is growing quickly and delivering strong profits, but limited high bandwidth memory (HBM) allocations constrain Instinct GPU sales, leaving Nvidia's lead wider. The excerpt estimates AMD's Q3 2026 revenue at $13.38 billion, up 44.7%, with Data Center revenue of $8.55 billion, up 96.9%; its CPU sales drove most of that segment's revenue, while modeled Instinct GPU sales were $3.43 billion. The author attributes AMD's gains against Intel in server CPUs to product design and its ability to scale manufacturing and packaging. AMD says it secured more supply and expects server revenue to double year over year in 2027, with datacenter exceeding 80% of total revenue. The author argues AMD can profit from the undersupplied AI market despite Nvidia's much larger scale, and proposes composable, reusable memory architectures as a possible way to ease resource constraints; the piece presents this as a speculative idea, not a planned product. ### Source excerpt Capitalism requires strong competition to function properly, and because of that, I have always been ... ## Operational resilience is becoming the new standard for financial services technology leaders DevFeed: [Operational resilience is becoming the new standard for financial services technology leaders]() Original publisher: [Read original article]() Author: Keith Sng Published: 2026-11-04T15:44:05Z Content type: article Language: en Sources: [Veeam Software Official Blog]() Topics: [Resilience](), [Disaster Recovery](), [Digital Trust](), [Automation]() Tags: [ai](), [asia-pacific](), [blog](), [business](), [but](), [compliance](), [conversations](), [data-resilience](), [financial](), [financial-services](), [first](), [i](), [it-s](), [matters](), [operational](), [operational-resilience](), [post](), [resilience](), [services](), [software](), [standard](), [technology](), [veeam](), [veeam-software](), [with]() ### AI overview The article's central point is that financial institutions need to prove critical services can recover within defined disruption tolerances; a successful backup restore or estimated RTO alone does not establish this. It recommends repeatable tests of end-to-end services and recovery records showing timing, dependencies, exceptions, outcomes, and accountability. Resilience evidence should connect data sensitivity and access with recoverability, including whether people or AI agents changed data and who is responsible. Recovery planning should also account for critical suppliers and concentrated dependencies. Regulatory frameworks differ across APJ markets, but the article says they share expectations for demonstrable, current resilience and governance accountability. ### Source excerpt Something has shifted in the conversations I have with financial institution technology leaders across APJ. It's subtle, but it matters. The post Operational resilience is becoming the new standard for financial services technology leaders appeared first on Veeam Software Official Blog. ## The 6 best WordPress booking plugins in 2026 DevFeed: [The 6 best WordPress booking plugins in 2026]() Original publisher: [Read original article]() Author: Ben Lyso Published: 2026-11-04T04:00:00Z Content type: comparison Language: en Sources: [Zapier]() Topics: [WordPress](), [Web]() Tags: [2026](), [app-picks](), [best](), [book](), [booking](), [but](), [card](), [counterfeit](), [credit-card](), [great](), [i](), [information](), [invest](), [like](), [logo](), [me](), [personal](), [personal-information](), [plugins](), [scam](), [wordpress]() ### AI overview WordPress has no built-in appointment booking, and the article recommends plugins for keeping booking and payment on the business's own site. Its six picks serve different needs: Amelia combines appointments and ticketed events, with several payment gateways but key features gated by plan; LatePoint emphasizes client self-service and includes all features on paid plans, with pricing based on site count; Simply Schedule Appointments suits solo professionals, though payments and team scheduling require higher editions; Booknetic supports unlimited locations, with many notifications and add-ons gated by plan; Bookly offers extensive add-ons and recurring bookings, but payments and self-service require paid add-ons; FluentBooking supports round-robin and collective team meetings without per-seat fees, with payments limited to Stripe and PayPal. The article evaluates setup, booking flow, availability and calendar sync, automation, and payments. It also describes connecting booking workflows to other apps through Zapier. ### Source excerpt I've been going to the same barber for years. He has his own shop now, and everything is great, but booking at his old shop was a mess. I'd click "Book now" and get transported to a completely different site with a strange logo, which asked me to enter every shred of personal information I had. It felt like walking down a dark alley where every turn could be a virus, a credit card scam, or a guy in a trench coat selling counterfeit watches. That's why a lot of businesses invest in a WordPress bo ## Monitoring AI agent sessions: Leveraging embedding models to aid threat hunting and attack discovery workflows DevFeed: [Monitoring AI agent sessions: Leveraging embedding models to aid threat hunting and attack discovery workflows]() Original publisher: [Read original article]() Author: Marcus Jeffes Published: 2026-11-04T00:00:00Z Content type: article Language: en Sources: [Elastic Security Labs]() Topics: [Threat monitoring](), [Embeddings](), [Threat Hunting & Intel](), [alert triage](), [AI Agent](), [Claude Code]() Tags: [agent](), [ai](), [ai-agent](), [ai-automation](), [attack](), [attack-discovery](), [discovery](), [embedding](), [i](), [leveraging](), [models](), [monitoring](), [more](), [my](), [one](), [prompt](), [questions](), [reasoning](), [sessions](), [soc](), [threat-hunting](), [workflows]() ### AI overview Embedding agent-session turns by channel and storing their vectors in Elastic enables repeatable semantic hunts against old data without rerunning an LLM over every session. In the author's six-month sample of 399 Claude Code and Cursor sessions (7,454 turns), an LLM labeler marked 56 real sessions for review; agent reasoning carried more incident signal than user prompts. Vector and BM25 search were close, with vectors performing better on the normalized data, while keyword search remained competitive for literal commands. Exemplar searches worked well for repeated instances of known workflows; written hunts were still needed to find different workflows with similar risks. The approach depends on suitable queries and thresholds, and results may not generalize: the sample was one person's security-focused work, the embedding model was not trained on agent transcripts, and some ATLAS areas showed weak retrieval. Claude Code telemetry omits reasoning, the strongest channel in this evaluation. The author also notes that vectors remain personal data and monitoring requires jurisdiction-specific legal review and access controls. ### Source excerpt I tested vector search on 7,454 turns of my own agent sessions. One in seven sessions needed review, the agent's reasoning carried more signal than the prompt, and the stored vectors answer questions nobody thought to ask at ingest. ## Artificial Limitation: The Illusion of Intelligence DevFeed: [Artificial Limitation: The Illusion of Intelligence]() Original publisher: [Read original article]() Author: Brad Nelson Published: 2026-11-03T19:07:59Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Artificial Intelligence](), [Machine Intelligence](), [Generative AI](), [Machine learning](), [AI Context Management](), [agent to agent communication](), [AI search](), [ai security]() Tags: [2025](), [2026](), [about](), [abstract](), [acquisition](), [actions](), [agent](), [ai](), [ai-hallucinations](), [ai-limitations](), [artificial-intelligence](), [artificial-limitation](), [but](), [call](), [can](), [doesn-t](), [generative-ai](), [hackernoon-top-story](), [i](), [illusion-of-intelligence](), [imitation](), [intelligence](), [learn](), [like](), [machine-learning](), [that-s]() ### AI overview The author argues that today's consumer AI models imitate intelligence but lack human-like learning, reflection, and executive control. They distinguish temporary in-context adaptation from lasting changes to model weights; training may use data later, but learned patterns are distributed and can lose their links to sources. The author says models often struggle to consistently apply many instructions, while probabilistic generation and hardware variation contribute to inconsistent outputs. A proposed workaround is to divide instructions among tiered skills, sub-agents, or an orchestrator, though this remains imperfect. The author treats AI output as requiring validation, including code, and is reducing its role in writing because responsibility for published work remains with the human author. They also acknowledge that how these systems work is not fully understood. ### Source excerpt Today's AI can imitate intelligence remarkably well, but it doesn't learn or reason like humans do. That's why I call it Artificial Imitation. ## Running Dry DevFeed: [Running Dry]() Original publisher: [Read original article]() Author: Ed Zitron Published: 2026-11-03T17:16:20Z Content type: opinion Language: en Sources: [Ed Zitron's Where's Your Ed At]() Topics: [Finance]() Tags: [1-4](), [1-5](), [1-6](), [17](), [2](), [2025](), [2026](), [3](), [about](), [absolute](), [accelerate](), [account](), [best](), [here](), [i](), [it-s](), [life](), [running](), [subscription](), [time]() ### AI overview The author argues that the AI boom is increasingly constrained by financing and weak underlying economics. OpenAI reportedly raised $11 billion at a $1.25 trillion valuation, below its earlier targets, despite large cash holdings and recent backing; the author reads this as evidence it may need to secure funding before markets tighten. Anthropic's amended filing, as quoted, shows Q3 compute costs rising faster than revenue and extensive compute and lease commitments, including large amounts whose timing or terms remain uncertain. The author says these obligations, possible below-investment-grade debt ratings, and dependence on venture-backed customers make both labs vulnerable if growth or funding slows. The piece also points to neoclouds losing bond-market access, Google Search monetization concerns, OpenAI's growing contribution to Microsoft revenue, and Grok Bot usage restrictions as signs that investors and providers are scrutinizing AI costs and returns. These are the author's interpretations of cited reports and filings; the text does not establish when or whether a market downturn will occur. ### Source excerpt If you haven't subscribed to Where's Your Ed At, now's the best time. I've extended the sale -- 10% off of the $70-a-year subscription for life (click here!) -- for new subscribers for another week. It's a ## I Ran My AI Judge on a Decentralized GPU. The Entire Bill Was Four Cents. DevFeed: [I Ran My AI Judge on a Decentralized GPU. The Entire Bill Was Four Cents.]() Original publisher: [Read original article]() Author: Chintan D. Published: 2026-11-03T13:56:38Z Content type: news Language: en Sources: [HackerNoon]() Topics: [AI Infrastructure](), [Cryptography](), [llm-reasoning](), [Ollama](), [observability](), [Test-driven development]() Tags: [about](), [account](), [agent](), [ai](), [ai-compute](), [ai-decisions](), [ai-inference](), [all](), [also](), [anchor](), [api](), [app](), [argument](), [bill](), [decentralize-ai-hackathon](), [decentralized](), [gpu](), [gpu-cloud](), [gpu-rental](), [i](), [my](), [nosana](), [open-source-gpu-cloud](), [policy](), [rtx](), [verifiable-ai]() ### AI overview VeriTrace's evaluation model returned "incorrect" for a $5,000 refund that violated a $2,000 policy limit, and its verdict was signed with a separate judge key, bound to the receipt, and verified with the receipt signature and Merkle proof. The 7B Qwen2.5 model ran on a rented Nosana RTX 3090 over a public HTTPS endpoint; the judge wrapper itself still ran locally, so the full evaluation service was not decentralized. The settled charge was $0.04 for a 12.5-minute deployment at $0.19/hour, including idle time; an initially visible $1.05 was a reservation, not a charge. The first inference took about 74.7 seconds while loading model weights, and the next took 1.04 seconds. The run demonstrates a verifiable record and policy agreement in this case, not that the model's judgment is necessarily correct. ### Source excerpt A 7B judge ran on a Nosana RTX 3090, caught a policy-breaking refund and signed its verdict into a receipt. The settled bill: four cents. ## Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data DevFeed: [Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data]() Original publisher: [Read original article]() Author: Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning]() Tags: [abstract](), [added](), [arxiv](), [best](), [but](), [cancer](), [classification](), [classifiers](), [cross](), [cross-validation](), [cs-lg](), [data](), [decision-rules](), [expression](), [fixed](), [help](), [hybrid](), [i](), [quality](), [rna](), [selection]() ### AI overview Across 10,469 TCGA tumours from 33 cancer types, logistic regression was the strongest and best-calibrated single learner, with balanced accuracy 0.943 on the locked test set. Stacking added at most 0.006 with 1,000 genes, but beat logistic regression in every fold with a minimal 100-gene panel; a hierarchical model did not reduce within-group confusion. Site-grouped validation reduced balanced accuracy by 0.027, mainly due to colon and rectal cancers. A breast cancer subtype panel transferred to independent SCAN-B data with accuracy 0.855, near the 50-gene PAM50 panel's 0.876. Five-gene stage I tumour-versus-adjacent-normal panels for nine cancers achieved held-out ROC AUC of at least 0.99 and detected most later-stage tumours. The results suggest ensembles help most with minimal panels, while a calibrated linear model suffices with 1,000 genes. ### Source excerpt arXiv:2611.00477v1 Announce Type: new Abstract: Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits. Starting from a quality-aware hybrid Boruta selector from earlier work, we developed and evaluated ensemble classifiers and cancer-specific gene signatures. Using 10,469 TCGA tumours of 33 types, we compared five learners and three ensembles under decision rules fixed in advance, reran the selection in site-grouped cross-validation, transferred a breast-cancer subtype panel to 3273 tumours of the independent SCAN-B cohort, and built five-gene stage~I tumour versus adjacent-normal panels for nine cancer types. Logistic regression (LR) was the most accurate and best-calibrated single learner (balanced accuracy 0.943 on the locked test set). Stacking added at most 0.006 with 1000 genes but improved on LR in every fold with a minimal 100-gene panel; a hierarchical model did not reduce within-group confusion. Site-grouped validation lowered balanced accuracy by 0.027, almost entirely in colon and rectal cancer (0.008 without them and the untestable LAML). On SCAN-B, the subtype panel reached 0.855, close to the 50 PAM50 genes (0.876). Stage~I panels reached ROC AUC >= 0.99 in held-out patients and detected most later-stage tumours. Ensembles help most with a minimal panel; with 1000 genes, a calibrated linear model suffices. ## Tight Stochastic Sequential Covers for General VC Classes with Unknown i.i.d. Features DevFeed: [Tight Stochastic Sequential Covers for General VC Classes with Unknown i.i.d. Features]() Original publisher: [Read original article]() Author: Changlong Wu, Wojciech Szpankowski Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Machine learning]() Tags: [2](), [abstract](), [adversarial](), [also](), [approximation](), [arxiv](), [bits](), [boolean](), [class](), [classes](), [cost](), [counts](), [covers](), [cs-lg](), [cube](), [d](), [features](), [general](), [i](), [stat-ml](), [vc](), [with]() ### AI overview For binary hypothesis classes, finite VC dimension alone supports tight distribution-blind stochastic sequential covers when features are unknown and i.i.d. The paper gives an exact cover with log-size O(d log T + log(1/δ)), matching the stated worst-case horizon dependence up to constants, and derives O((d+1) log T) expected worst-case log-loss regret. Its construction weights splits by how many Boolean subcubes meet each realizable trace; a coordinate-deletion inequality bounds their total cost using VC dimension. The authors also report an exact polynomial-time implementation for planar affine halfspaces. This result does not extend to pseudo-dimension generally: a fixed pseudo-dimension-one sigmoid class needs Ω(log² T) bits for uniform sequential approximation at accuracy 1/T. ### Source excerpt arXiv:2611.00788v1 Announce Type: new Abstract: We study hybrid online learning with stochastic features and adversarial labels when the feature distribution is unknown. Prior work gave stochastic sequential covers of log-size $O(d\log^2 T)$ for a general VC class and logarithmic bounds only under stronger star-type structure. We show that finite VC dimension alone is enough: every binary class of VC dimension $d$ admits a distribution-blind exact cover with log-size $O(d\log T+\log(1/\delta))$, matching the worst-case $\Omega(d\log(T/d))$ dependence on the horizon. The key ingredient is a new local cube weight that counts Boolean subcubes incident to each realizable trace. Unlike the equal splits used in earlier constructions, we split mass unevenly according to these cube weights; a coordinate-deletion inequality shows that VC dimension controls the total cost of these splits. This yields, to our knowledge, the first tight $O((d+1)\log T)$ expected worst-case log-loss regret for general VC partitions with unknown i.i.d. features, and an exact polynomial-time implementation for planar affine halfspaces. We also show that the binary phenomenon does not extend from VC dimension to pseudo-dimension: a fixed pseudo-dimension-one sigmoid class requires $\Omega(\log^2 T)$ bits for uniform sequential approximation at accuracy $1/T$. ## What makes a theorem interesting? Operationalizing interestingness for open-ended exploration DevFeed: [What makes a theorem interesting? Operationalizing interestingness for open-ended exploration]() Original publisher: [Read original article]() Author: Silv\\\`ere Gangloff Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Coq](), [AI Chat]() Tags: [abstract](), [agent](), [also](), [approach](), [arxiv](), [can](), [canon](), [community](), [cs-ai](), [dependencies](), [exploration](), [features](), [formal](), [formalized](), [i](), [ideas](), [knowledge](), [language](), [language-model](), [large](), [open]() ### AI overview The study operationalizes theorem interestingness by learning from results mathematicians single out in formal libraries and papers. In formalized mathematics, features available before evaluation--especially statement and proof length and the subfields used in proofs--predict those selections about as well as a large language model judge, and the two approaches complement each other. Most of the signal is shared and transfers partly across formal libraries; remaining differences reflect practices and individual authors more than subfields. The judge's advantage comes from knowledge of the mathematical canon, while neither method relies on remembering the papers. The abstract suggests this way of inferring community preferences could extend to other sciences. ### Source excerpt arXiv:2611.02905v1 Announce Type: new Abstract: An agent that explores, whether it generates conjectures or research ideas, must decide which of them are worth keeping. This judgment of interestingness is usually delegated to a hand-picked proxy or to a language model, neither of which is known to correspond to what humans actually value. I study it for mathematical theorems, by observing which results mathematicians single out in formal libraries and in papers. I model each way of singling out results as an evaluator that weights features of a result's statement, its proof and dependencies, and split its weights into a shared and a subjective part. In formalized mathematics, ex-ante features predict these selections as well as a large language-model judge, and the two complement each other. Interestingness is also largely shared. One set of weights, carried mostly by structural features such as the length of the statement and of the proof and the subfields the proof draws on, accounts for most of the signal and transfers in part across formal libraries. What remains subjective depends on practices and individual authors more than on subfields. The judge's advantage lies in its knowledge of the canon, and neither the judge nor the features rely on memory of the papers. The approach, which infers what a community finds interesting from what it singles out, can be applied to other sciences. ## 1M to $100M Run Rate in 4 Quarters DevFeed: [1M to $100M Run Rate in 4 Quarters]() Original publisher: [Read original article]() Author: Ivan Burazin CEO; Co-Founder Published: 2026-11-03T00:00:00Z Content type: article Language: en Sources: [Dotfiles Insider]() Topics: [AI Infrastructure](), [AI Sandbox](), [virtual machines](), [API Monetization]() Tags: [2](), [2023](), [2026](), [3](), [about](), [agent](), [agent-workflows](), [agents](), [ai](), [ai-agents](), [ai-native](), [am](), [browser-based-ide](), [browser-ide](), [cloud-coding](), [cloud-dev-environment](), [cloud-development-environment](), [cloud-editor](), [cloud-ide](), [cloud-programming](), [code-in-cloud](), [code-on-web](), [coding-environment](), [days](), [i](), [ide-editor-online](), [ide-in-browser](), [ide-online](), [keep](), [numbers](), [october](), [online-code-editor](), [online-ide](), [online-programming](), [online-web-dev-ide](), [online-web-development-ide](), [projection](), [run](), [sde](), [standardised-development-environment](), [stories-insights](), [this](), [web-based-coding](), [web-ide]() ### AI overview Daytona says it grew from a $1M to a $100M run rate in four quarters after rebuilding its product for AI agents. The article attributes demand to agents needing isolated, stateful computers for code execution, browser use, package installation, and long-running jobs. It says traditional cloud workloads do not match agents' need to start in tens of milliseconds, pause and resume, and run many environments or parallel branches. Daytona's Agent Execution Cloud offers instantaneous startup, persistent state, forks and snapshots, and per-second billing. The company describes volatile reinforcement learning and evaluation traffic as a capacity challenge. It compares its growth timeline with several AI companies, while noting that a run rate is a projection and not a destination. ### Source excerpt Four quarters. That is how long it took us to go from a $1M run rate to $100M. This year alone, we grew 37.5x. I keep staring at those numbers. They still do not feel real. Checking our projected run rate every morning has become an addiction. Ten days into October, it was already showing $100M. I knew it was a projection. I knew there was still a real chance we wouldn't finish the month there. ## FloppoScope is a Disk Image Analyzer DevFeed: [FloppoScope is a Disk Image Analyzer]() Original publisher: [Read original article]() Author: Zoe Skyforest Published: 2026-11-01T15:00:36Z Content type: article Language: en Sources: [Hackaday]() Topics: [Disk image]() Tags: [amiga](), [apple](), [atari](), [bbc](), [browser](), [disk-image](), [dos](), [filesystem](), [floppy-disk](), [flux](), [hard-drive](), [i](), [pc](), [retro-tech](), [retrocomputing](), [sensitive-data](), [sharp](), [software-hacks](), [web-based]() ### AI overview FloppoScope is a browser-based floppy disk image analyzer that runs locally. It maps disk layouts with color coding, displays filesystem data, previews common file types, converts between image formats, and reports detected bad sectors or corrupted files. The text says it supports PC, Apple, Amiga, Atari DOS, BBC Micro, Sharp MZ, and NeXTSTEP formats, plus both disk image and flux files. ### Source excerpt If you're a big retro tech fan, you might still have a floppy drive on hand, or even some sort of high-end disk imaging solution. If you're a little less ...read more ## How to Make Text Italic in HTML (i vs em vs CSS) DevFeed: [How to Make Text Italic in HTML (i vs em vs CSS)]() Original publisher: [Read original article]() Author: Marko Published: 2026-11-01T09:00:00Z Content type: tutorial Language: en Sources: [Marko Denic Blog]() Topics: [HTML](), [CSS](), [Font](), [markup](), [Variable Fonts](), [Markdown](), [selectors]() Tags: [css](), [em](), [examples](), [how-to](), [html](), [i](), [tutorial]() ### AI overview Choose `` for words stressed for emphasis, `` for text in an alternate voice such as foreign words or technical terms, and CSS `font-style: italic` when italics are purely visual. These elements look alike by default but convey different meanings; use `` for importance rather than stress, and use ``, ``, ``, or `
` for their stated meanings when appropriate. CSS can also reset italics with `font-style: normal` or create a slant with `oblique`. If a web font lacks an italic file, browsers may fake italics by slanting regular letters; loading an italic font style or using a variable font with an italic axis avoids that fallback. The supplied text notes that screen readers usually do not change their voice for ``, so important meaning should not rely on italics alone. ### Source excerpt Make text italic in HTML: when to use em, when to use i, the other tags that render in italics, the CSS font-style property, and how to remove italics. With examples. [Next page]()