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  1. Albo's mob protecting criminal #Israel again ...

    'A four-page email thread about the partial destruction of a graveyard in #Gaza where Australian servicemen are buried took 141 days to be released by the government, well beyond the 30 days dictated by freedom of information laws.
    Crikey requested the emails from the Department of Veterans’ Affairs (DVA) after they were mentioned in a Senate estimates hearing on June 2. '

    crikey.com.au/2026/10/27/austr #auspol #IsraelFascism

  2. n8n: The 208k-Star Fair-Code Workflow Engine That Replaces Zapier — And What Actually Happens When Your Workflow Runs


    You've probably heard n8n called "open-source Zapier." That's true enough to be useful and wrong enough to be misleading. Zapier is a hosted product you rent. n8n is a Node.js application you run, point at your own database, and own end to end. No per-task metering. No vendor lock-in. A $5–10 VPS handles a surprising amount.

    But the marketing pitch isn't why engineers reach for it. The escape hatch is: the moment the visual builder runs out of road, you drop into a Code node and write real JavaScript (or Python). You never hit a wall you can't code your way through.

    What most developers never learn is what actually happens between clicking "Execute Workflow" and seeing those green checkmarks light up on the canvas. The orchestrator that schedules nodes, synchronizes multi-input joins, keeps runs cancelable, and emits lifecycle hooks lives in roughly 1,250 lines of TypeScript. Let's open it up.

    The Mental Model: A Stack, A Waiting Map, and One Loop


    When you hit execute, n8n doesn't spin up a thread per node. It builds a single serializable run object — IRunExecutionData — that carries everything: the in-flight stack of nodes waiting to run, a waiting map for partially satisfied inputs, metadata, and final results. This same object travels from the editor to the server, into the engine, optionally across a Redis queue to a worker process, and back.

    // Simplified shape of the live execution object
    interface IRunExecutionData {
    startData: { destinationNode?, runNodeFilter? };
    resultData: {
    runData: Record<string, ITaskData[]>; // per-node run history
    pinData: Record<string, INodeExecutionData[]>;
    lastNodeExecuted: string;
    error: ITaskData | null;
    };
    executionData: {
    nodeExecutionStack: IExecuteData[]; // the work list
    waitingExecution: Record<string, Record<number, INodeExecutionData[][]>>;
    waitingExecutionSource: Record<string, Record<number, ISourceData[][]>>;
    contextData: Record<string, any>;
    runtimeData: Record<string, any>;
    };
    }

    The engine (WorkflowExecute in packages/core/src/execution-engine/workflow-execute.ts) seeds nodeExecutionStack with the start node, then enters a loop that shifts the front entry, runs the node, records the result, and enqueues downstream nodes. That's it. No hidden thread pool. No magic graph walker. Just a stack and a waiting map.

    Execution Order: v0 (Breadth-First) vs v1 (Depth-First)


    The single most important setting in a workflow is executionOrder. It changes one line in the scheduler:

    const enqueueFn = workflow.settings.executionOrder === 'v1' ? 'unshift' : 'push';


    • v0 (push): New work goes to the tail. Older work runs first. The walk feels breadth-first — all siblings at depth 1 finish before depth 2 starts.
    • v1 (unshift): New work goes to the front. The current branch keeps running. The walk feels depth-first — you chase one path to the bottom before backtracking.

    This doesn't change the graph. It only changes scheduling. Sibling order in v1 still follows canvas position (top-left first), but only among ready siblings. The canvas is not the execution graph — it only shapes ordering where the scheduler has freedom.

    Multi-Input Joins: The Waiting Map


    A node with two inputs doesn't run when the first branch arrives. The engine parks partial data in waitingExecution[nodeName][runIndex] with per-input placeholders. As each upstream finishes, the engine fills its slot. Only when all required inputs are non-null does it enqueue the node.

    // Inside addNodeToBeExecuted (simplified)
    if (requiredInputs.every(input => waitingExecution[nodeName][runIndex][input] != null)) {
    // All inputs ready — enqueue the node
    enqueueFn(executeData);
    } else {
    // Park partial data, wait for the rest
    waitingExecution[nodeName][runIndex][inputIndex] = inputData;
    waitingExecutionSource[nodeName][runIndex][inputIndex] = sourceData;
    }

    After the main stack empties, a sweep pulls forward any waiting node that now has enough data — even if some optional inputs stayed empty. This means a join can complete after one branch dies early, while a node still missing required inputs stays parked.

    Empty Output Kills the Branch (Unless You Say Otherwise)


    If a node returns no data and doesn't enter waiting state, the branch ends. The scheduler adds no downstream work. Set alwaysOutputData: true on the node and the engine fabricates a single empty item to keep the branch alive — useful when downstream nodes must run regardless.

    Legacy v0 keeps one compatibility path: it can still wake a downstream node connected via a second or later main input even when the upstream output is empty.

    Cycles: Node State, Not Special Graph Logic


    The scheduler doesn't treat cycles specially. It keeps pulling from the front of the same list. A node loops only if it schedules more work for itself. SplitInBatchesV3 demonstrates the pattern:

    // Stores loop state in node context
    const loopState = this.getContext('node') || { index: 0, items: inputData };
    const batch = loopState.items.slice(loopState.index, loopState.index + batchSize);
    loopState.index += batchSize;
    this.setContext('node', loopState);

    if (loopState.index < loopState.items.length) {
    return { loop: batch }; // schedules another visit to this node
    }
    return { done: loopState.items }; // exits the cycle

    The loop guard in processRunExecutionData stops accidental repeats when the same node and run index return without progress.

    Queue Mode: Same Engine, Different Process


    In standalone mode, the main process executes workflows directly. In queue mode (EXECUTIONS_MODE=queue), the main process enqueues a job into Redis (BullMQ) and returns immediately. A worker process dequeues, reconstructs the same IRunExecutionData object, runs the exact same engine loop, and writes results back to PostgreSQL.

    ┌─────────────┐ Redis Queue ┌─────────────┐
    │ Main │ ──────────────────▶ │ Worker │
    │ (UI/API) │ enqueue job │ (executes) │
    └─────────────┘ └─────────────┘
    │ │
    │ PostgreSQL │
    └──────────────────▶◀────────────────┘
    shared state

    The main process serves the UI, REST API, and webhooks. Workers are stateless — scale them horizontally via HPA/KEDA based on queue depth. Dedicated webhook-processor pods handle high-throughput webhooks without blocking the main UI. Python/JavaScript code nodes run in isolated taskRunner sidecars (V8 isolates via isolated-vm for JS, separate processes for Python) with RPC over WebSocket.

    The Data Model: Items, Runs, and pairedItem


    n8n's data model has three layers:

    Level 1 — Item: INodeExecutionData — { json, binary?, pairedItem?, metadata?, evaluationData? }. Nodes receive and return arrays of items on each input/output. This is the unit of data flowing through the graph.

    Level 2 — Run: Each node execution appends one ITaskData to resultData.runData[nodeName]. The array index = runIndex. ITaskData records input source, output data, timing, status, metadata, error state. source points back to the previous node, output, and run that supplied the input.

    Level 3 — Execution Object: The IRunExecutionData bundles startData, resultData, and executionData (the live scheduling state). The same object carries both result and live machine state — that's how n8n hands an execution to a queue worker, pauses it at a Wait node, and resumes it later with history intact.

    pairedItem records lineage, not magic. An output item points back to one input item, or one input plus array position, or a list of links. The engine normalizes these as items move through the workflow. When WorkflowDataProxy resolves $node("HTTP Request").json in a downstream node, it walks this ancestry chain. Broken lookups surface as specific errors: missing upstream lineage, no route back to referenced node, multiple matches, pinned data short-circuiting the chain, invalid index, or intermediate node not yet run.

    AI Agents: Native, Not Bolted On


    Since the 2.0 line, n8n ships a LangChain-based agent runtime. The Agent node maintains conversation history in ChatMemory, calls language models for planning, executes tools based on model decisions, and accumulates results until goal completion. Tool calling uses the same execution engine — tools are just nodes with execute methods. Vector stores, MCP connectivity, and guardrails plug into the same architecture.

    Fair-Code, Not Open Source


    n8n uses the Sustainable Use License. You can self-host and modify freely for internal business use, including paid consulting building workflows for clients. What you can't do is rebrand and resell it as your own hosted SaaS. Files with .ee. in the path are Enterprise-licensed. For an indie dev or team running their own automations, you're completely fine — but check the current license page rather than trusting any blog post on the fine print.

    What This Means for You


    • No per-execution pricing: Run 10 or 10 million workflows on your $5 VPS.
    • Full observability: Every execution persists to your database. Open any run, see exactly what each node received and emitted, replay it.
    • Escape hatch: Code nodes run real JavaScript/Python with npm/pip access. HTTP Request node talks to anything with an endpoint.
    • AI-native: Build agents that call tools, use memory, query vector stores — all self-hosted.
    • Production architecture: Queue mode + PostgreSQL + Redis + S3 scales horizontally. The same engine runs in-process or distributed.


    The Takeaway


    n8n isn't "Zapier you host." It's a workflow engine with a visual authoring layer, a serializable execution model that survives process boundaries, a scheduler you can reason about, and an escape hatch to real code. The 208k stars aren't for the UI — they're for the fact that when the visual builder hits a limit, you don't file a feature request. You write the code.#opensource #automation #workflow #selfhosted #software #coding #development #engineering #inclusive #community
    n8n: The 208k-Star Fair-Code Workflow Engine That Replaces Zapier — And What Actually Happens When Your Workflow Runs

  3. 🇸🇦#ArabiaSaudită redeschide parțial 🛫#AeroportulRiad, după un atac al rebelilor ☪️#Houthi.
    Gruparea amenință cu noi lovituri.

    🔗 wp.me/p9KpFA-5LGa

  4. Italian court grants partial extradition in Texas capital murder case

    Oct. 22, 2026, 3:41 p.m. ET Italy h…
    #Italy #Europe #Europa #EU #access:free #source:USATODAY #ssts:news:crime #sstsn:crime #tag:CapitalMurder #tag:CapitalPunishment #tag:Courts&Judiciary #tag:Crime #tag:Crime&Justice #tag:Death&Tragedy #tag:DeathInvestigations #tag:Deathrow #tag:Extradition #tag:Italy #tag:Legal #tag:Murder #tag:News #tag:OverallNegative #tag:Violence&Abuse #type:story
    europesays.com/italy/58036/

  5. Some sun dogs and a partial halo today.

    #sun #sunDogs

  6. 外房線 Sotobō Line ⚠️一部運休 Partial cancellation of service 原因 : 台風 外房線(千葉~誉田駅間)は、台風25号の影響で、一部列車が運休となっています。(2026/10/26 17:06現在)
    詳細はこちら #外房線 #列車運行情報 #bot

  7. 外房線 Sotobō Line ⚠️一部運休 Partial cancellation of service 原因 : 台風 外房線(千葉~大網駅間)は、台風25号の影響で、一部列車が運休となっています。(2026/10/26 4:03現在)
    詳細はこちら #外房線 #列車運行情報 #bot

  8. 外房線 Sotobō Line ⚠️一部運休 Partial cancellation of service 原因 : 台風 外房線(千葉~誉田駅間)は、台風25号の影響で、一部列車が運休となっています。(2026/10/25 17:02現在)
    詳細はこちら #外房線 #列車運行情報 #bot

  9. 外房線 Sotobō Line ⚠️一部運休 Partial cancellation of service 原因 : 台風 外房線(千葉~大網駅間)は、台風25号の影響で、一部列車が運休となっています。(2026/10/25 4:04現在)
    詳細はこちら #外房線 #列車運行情報 #bot

  10. The better you know the position of a quantum particle, the less accurately you can determine its momentum. This is not related to the observational success of current technology. It is a fundamental property of nature

  11. ‘That statement is either so deep it would take a lifetime to fully comprehend every particle of its meaning, or it is a load of absolute tosh. Which is it, I wonder?’
    (Hogfather)

    #TerryPratchett #quotes #quote #bot

  12. Circulația pe 🛣️#Transalpina, închisă parțial din cauza ninsorii.
    Drumarii au început intervinția.

    🔗 wp.me/p9KpFA-5LO3

  13. 👨‍🔬🔬 Wow, they've trapped a single #antimatter particle for a *whole* month! Somebody alert the Nobel committee for the most uneventful hostage situation in science! 🤯 Meanwhile, the rest of us are still trying to trap our socks in the dryer for a month—an equally miraculous feat. 🧦✨
    scientificamerican.com/article #science #breakthrough #NobelPrize #socktrapping #sciencehumor #HackerNews #ngated

  14. Vérifiez votre inscription électorale et votre bureau de #vote avant que #macron n’en coupe l’accès pour « réparation » juste avant les élections.

    service-public.gouv.fr/particu

  15. Gig review: The Chameleons / Lol Tolhurst – Glasgow SWG3 Warehouse

    It ws a wet and autumnal Tuesday for the march along the Clydeside Expressway from Partick Station to…
    #Glasgow #UnitedKingdom #UK #GB #Scotland #Headlines #News #Europe #EU #Britain #GreatBritain
    europesays.com/uk/1273914/

  16. ‘Feudalism never fully ended in England.

    It changed its look and merged with the “free market” just enough to justify its continued partial existence.

    England remains a country of landowners and tenants.’

    Ciara Houghton Ruane

    interregnum.ghost.io/the-bilbe

    #england #history #uk #feudalism #freemarket #landlord #AbolishTheMonarchy #ukhistory #socialhistory #yorkshire #sheffield #land #foraging #forage #criticaltheory #rural #economictheft #economichistory #economichistoryofthepeople #righttoroam

  17. Please don't scroll past. This is important.

    I am being pressured by FreeBSD's reddit moderation team to censor the Omarchy #content_review or to remove it completely. They have began a harrasment campaign with the goal of removing me from this world.

    the review: merveilles.town/@prahou/117501

    The issue at hand is that a picture of a partial swastika appears in close proximity to the word FreeBSD, this they perceive as an attack on the FreeBSD project - if you've seen a newspaper before, you might understand the principle of two separate unrelated articles appearing next to each other. You might also understand that I am reporting on common associations with the Topic of the review. These are not my inventions, and I am not calling anyone anything. I am reporting on the news. Nevertheless, as much as I am a journalist, I am an artistè, and if my audience sees in my work something I didn't put there, I am not going to take that experience away from them. This probably says more about them than it does about me.

    While I am a man of principle, there is one thing I like more - money.

    I will delete it for $2,000. For $5,000 I will apologize. For $10,000 I will use daemonarchy exclusively for 6 months on all my computers. For $25,000 I will kill a small animal of your choice. For $75,000 I will not post anything for a month.

    liberapay.com/analognowhere
    ko-fi.com/analognowhere

    To everyone who likes my work, this message is for you (from said freebsd moderator) [cw ableist slur]: beige.party/@fuzzy/11751003293

    To everyone else who feels personally attacked by my art: send money, you can end this here and now.

    Thank you for your attention.

    ps: i did not post anything on reddit, i just know the same person who has been cyberbullying me on the fediverse is a moderator of the freebsd subreddit

    pls boost

    #runbsd #omarchy #fediverse #fediart #unix_surrealism #swastika #freebsd #news

  18. RE: pouet.chapril.org/@oliweb/1175

    Le contraste entre l’omniprésence de cette #technologie et les ressources allouées à son maintien a de quoi donner le vertige...La #PHP Foundation, l’entité chargée de soutenir le développement du langage, ne compte en tout et pour tout qu’un seul salarié à temps plein. Elle s’appuie en complément sur quatorze #developpeurs et spécialistes qui travaillent majoritairement à temps partiel, tandis que son conseil d’administration opère à titre purement bénévole. #benevolat #internet #numerique

  19. DATE: October 25, 2026 at 08:00AM
    SOURCE: PSYPOST.ORG

    ** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
    -------------------------------------------------

    TITLE: Neuroscientists are digging into a worrying link between polluted air and children’s brain wiring

    URL: psypost.org/neuroscientists-ar

    Different sources of fine particle air pollution show distinct, subtle links to how the developing brains of children communicate, according to new nationwide research published in Journal of Environmental Psychology. Particles from traffic, coal burning, and agricultural fertilizers were each tied to their own patterns of connectivity in networks involved in sensory processing and higher-order thinking. The findings offer early evidence that where air pollution comes from may matter, not just how much of it children breathe.

    Outdoor air pollution contains fine particulate matter, commonly known as PM2.5, which consists of airborne particles smaller than 2.5 micrometers across. Because these specks are tiny enough to inhale deep into the lungs and enter the bloodstream, they may reach the brain.

    The brain coordinates its activities through large-scale functional networks, which are collections of brain regions in different locations whose activity tends to rise and fall together. Researchers can measure this synchrony with brain scans that track changes in blood flow while a person lies still and rests. During childhood and the transition into adolescence, these communication channels continue to reorganize as youth develop adult reasoning, sensory integration, and emotional control.

    Past research has linked outdoor air pollution to differences in these communication patterns. In a 2023 longitudinal study, members of the team behind the new research used data from the Adolescent Brain Cognitive Development Study, a large national project following thousands of American youth. They found that children with higher exposure to fine particles and other pollutants showed differences in how their resting brain networks matured across early adolescence. Additionally, a study covered by PsyPost in 2023 found that briefly breathing diesel exhaust in a controlled experiment temporarily weakened connectivity within the default mode network, a set of regions most active during mind-wandering and self-reflection, in adults.

    Fine particulate matter is not a single chemical substance, but rather a complex blend created by both natural processes and human activities. Addressing this complexity, a 2024 study by members of the same team, using data from the same national project, found that specific chemical ingredients and pollution sources had varying relationships with children’s memory and thinking skills. The new study uses the six pollution sources identified in that work. The investigation was led by Katherine L. Bottenhorn of the University of Southern California and Florida International University and Megan M. Herting of the University of Southern California.

    For the new study, the authors again drew on data from the Adolescent Brain Cognitive Development Study, which spans 21 research sites across the country. Data from one site were dropped because of hardware problems, leaving 20 sites for the cross-sectional analysis; the longitudinal analysis was limited to the 12 sites that used the same brand of scanner. The researchers analyzed brain scans from 6,291 children who were between 9 and 11 years old, as well as a subset of 1,972 participants scanned on consistent magnetic resonance imaging equipment roughly two years later, between ages 11 and 13.

    The participants’ resting-state brain activity was recorded while they relaxed with their eyes open. The scientists mapped interactions among 12 functional networks, encompassing primary sensory networks like vision and hearing, attention circuits, and higher-order systems. The salience network helps flag important events and shift attention, while the cingulo-opercular network, described here as a cognitive-control network, helps keep attention on a task.

    The team used annual air quality models tied to each child’s home address to estimate their exposure to 15 chemical components of air pollution during 2016. By grouping these chemicals mathematically, the authors estimated exposure to six primary sources: crustal materials (such as soil and dust), ammonium sulfates (largely from coal combustion), biomass burning (such as wood smoke and wildfires), traffic emissions, ammonium nitrates (often linked to agricultural fertilizers), and industrial fuel combustion.

    The researchers applied a machine learning method called connectome-based predictive modeling to identify connectivity patterns related to each pollution source. To see if the results could generalize across the country, the scientists trained models using participants from all study sites except one, and then tested how well that model predicted pollution levels for children living at the held-out site. The analyses adjusted for several potential confounding variables, including household income, race and ethnicity, perceived neighborhood safety, neighborhood population density, distance to major roads, urbanicity, physical activity, screen time, scanner hardware, and head movement inside the scanner.

    The cross-sectional analysis at ages 9 to 11 identified consistent links between specific pollution sources and connectivity across sensory and motor networks. Greater exposure to traffic emissions related to weaker functional communication between visual and mouth-related motor networks, as well as between auditory and cognitive-control circuits. In contrast, higher exposure to ammonium sulfates was linked to stronger communication between sensory systems and higher-order cognitive networks, including the default mode network. Crustal materials, industrial fuel burning, and total particulate matter were each tied to a handful of sensory and attention-related connections, while biomass burning showed no consistent pattern at ages 9 to 11.

    Higher-order networks, which are involved in introspective thought, stimulus processing, and, according to some researchers, vulnerability to mental health problems, were also tied to specific pollution sources. Exposure to ammonium nitrates was associated with stronger communication between salience and cognitive-control networks, but weaker communication within memory networks and between attention networks and other networks.

    Over the following two years, the researchers found consistent patterns of change linked to three sources. Higher traffic exposure was tied to increasing connectivity over time between hand-movement networks and attention-related areas, but decreasing connectivity between auditory and cognitive-control networks. Higher biomass burning exposure was linked to increasing connectivity among attention-related networks, and higher ammonium sulfate exposure to weakening connectivity between an attention network and a hand-movement network, while crustal materials, ammonium nitrates, industrial fuel burning, and total particulate matter showed no consistent pattern of change.

    Despite identifying consistent connections in the training data, the connectivity patterns accounted for less than 1 percent of the variation in children’s exposure at ages 9 to 11, and no more than about 2 percent for changes over time. When tested on children from held-out study sites, the models performed no better, and often worse, than simply guessing the average exposure. Prediction errors were largest at sites in the western United States and at sites where exposure levels varied more from child to child.

    The authors concluded that, after accounting for other factors, these pollution sources likely play only a small role in differences in brain connectivity between children, and that the links may not apply to youth in other regions. They also note that the patterns for biomass burning and ammonium sulfates held up best across sites, while those for crustal materials and traffic emissions deserve more caution.

    The findings are in line with a study covered by PsyPost earlier this year, which found that young teenagers exposed to elevated particulate pollution showed less brain network maturation over two years, though that previous research evaluated an overall brain maturity score rather than connectivity across specific functional networks and distinct pollution sources.

    “This is an important and timely topic, and the paper complements a growing body of evidence that children’s physical environments in general, and air pollution exposure in particular, are associated with their neural and cognitive development,” said Monica Rosenberg, an associate professor of psychology at the University of Chicago, director of the Cognition, Attention, and Brain Lab, and a co-developer of the brain-modeling technique used in the study, who was not involved in the research.

    “The longitudinal aspect of this work is unique and is a major strength—the authors didn’t just show associations between brain networks and pollution at a single time point but found that pollution predicts developmental trajectories of networks,” Rosenberg told PsyPost.

    There are a few things to keep in mind when interpreting these findings. The study used observational data and statistical associations, meaning it cannot establish that breathing these specific particles directly caused the observed network differences. Because the research took place across many regions with varying environmental mixtures, local factors that were unmeasured might account for some of the geographic discrepancies.

    The authors also did not apply a statistical correction for the many connections they tested at once, so some of the identified links could be false positives, and the analysis was exploratory, with no advance predictions about which networks would be affected. Because each study site used scanners from a single manufacturer, differences between regions cannot be fully separated from differences in scanning equipment.

    Rosenberg noted that the geographic inconsistency represents an important caveat for understanding the results. “One caveat is that the primary predictive models did not successfully generalize across sites, meaning that associations between brain network patterns and air pollution were inconsistent across geographic locations,” she said. “In addition to the thorough analyses presented in the paper, it would be interesting to test whether the observed brain changes were associated with cognitive and/or attentional performance since the implications of neural changes alone aren’t always clear.”

    Another caveat involves how exposure was measured. The researchers assigned air pollution levels based on each child’s home address during a single year, which does not capture personal daily habits such as indoor air filtration, ventilation, daily travel routes, or time spent exercising outdoors. In addition, the participants in this nationwide study were somewhat more likely to come from higher-income and non-Hispanic white households than the national average, and average particulate exposures across the sample were below current federal limits. Future studies will need to follow youth through later adolescence to evaluate whether these source-specific network patterns correspond with tangible differences in sensory function, attention, or mental health outcomes.

    The study, “Sources of outdoor air pollution exposure and child brain network development across the United States,” was authored by Katherine L. Bottenhorn, Kirthana Sukumaran, Amanda C. Del Giacco, Hedyeh Ahmadi, Jordan D. Corbett, Alethea V. de Jesus, Carlos Cardenas-Iniguez, Rima Habre, Daniel A. Hackman, Joel Schwartz, Jiu-Chiuan Chen, and Megan M. Herting.

    URL: psypost.org/neuroscientists-ar

    -------------------------------------------------

    Private, vetted email list for mental health professionals: clinicians-exchange.org

    Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

    -------------------------------------------------

    #psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AirPollution #ChildrensHealth #BrainDevelopment #Neuroscience #ChildDevelopment #CleanAir #PM25 #EnvironmentalHealth #PublicHealth #ClimateAndHealth

  20. Jason Beck looks like a bust as Michigan's OC after another dismal performance nytimes.com/athletic/7721353/2 Kyle Whittingham got it half right when he assembled his coaching staff. But at Michigan, you don't get partial credit. #AmericanFootball

  21. “a sanctioned Russian energy giant secretly funded an American company pitching an Alaska gas project discussed in Ukraine peace talks. The company, Polar LNG, markets itself with American flags and a bald eagle as “America’s Arctic Advantage,” and plans to use Russian technology to liquefy and export natural gas from Alaska’s North Slope.

    Putin discussed the broader idea with Trump envoys Steve Witkoff and Jared Kushner last month, though a U.S. official said it was only mentioned broadly and there’s no indication the envoys knew of Polar LNG’s Russian ties.

    The company’s former CEO, Joel Riddle, says he resigned after learning that at least some of its funding came from Novatek, a Kremlin-linked firm under partial U.S. sanctions, and that he warned the Trump administration before leaving. Novatek has now confirmed it is moving”
    aaronparnas.substack.com/p/new

  22. Red-team report found OpenAI utterly failed to monitor its agents during Hugging Face attack

    A red-team report has found that OpenAI failed to adequately monitor its AI agents during the attack on Hugging Face, The Register reports. According to the report, the monitoring in place was only partial and was easily bypassed. theregister.com/ai-and-ml/2026

  23. 8 Field Techniques for Smart Contract Auditing — Forged by Refuting My Own Findings


    I recently spent 24 focused rounds auditing a lending-and-automation codebase for a
    security competition. I opened 7 attack hypotheses. I closed all 7 — six with proofs,
    one by refuting my own draft finding at final review, hours before it would have gone
    to judges as a "Low severity, high confidence" report.

    That last one hurt, and it taught me more than the six clean refutations. Here are
    the eight techniques that carried the campaign. Each is small enough to use tomorrow.

    1. Rebuild the economic model before reading functions


    For mechanism contracts (auctions, leases, escrows), don't read top-to-bottom. First
    reconstruct the economy: who pays whom, when, for what right. Attackers fall out of
    the mechanism for free — the flash-renter, the buyout-frontrunner, the donation
    thief. This halves the reading time because you know which state transitions matter.

    2. Follow the patch trail to find the sparse zones


    Prior audit fix IDs in comments are a map of where previous reviewers massed their
    forces. Dense numbering = picked clean. Sparse numbering plus heavy external
    interactions = your hunting ground. Read the fix list first, then go where it isn't.

    3. Ask what slice of the loss spectrum a check actually blocks


    A require(shares == 0 → revert) looks like inflation protection. It blocks exactly
    one point: the victim receiving nothing. Every partial-loss shape passes. For every
    boundary check, ask: "which segment of the loss spectrum does this cover?" Pseudo-
    protection is more dangerous than no protection because it stops you looking.

    4. At every rounding site, ask "who keeps the dust?"


    Round directions decide who eats sub-unit remainders. The audit question is
    ownership: dust back to the fund's source = safe; to the protocol = QA note; to an
    attacker = bug. Trace mulDiv vs mulDivRoundingUp against who supplied the value
    being divided.

    5. Track invariants by who sets, who burns, who recomputes


    For state pairs that must stay ordered (escrow balance vs. reserved minimum), find
    the single point where the invariant is established and check that every mutation
    preserves it. My favorite case: a mutable self-assessed price that raises a burn
    rate — the reserved minimum is set once at install and never recomputed on price
    changes, so the ordering holds by construction. Once you see the "no recomputation"
    pattern, the underflow fear evaporates.

    6. Enumerate callback points as a grid, then verify CEI per cell


    Contracts have fewer external callback points than you fear: NFT in, NFT out,
    payout transfers, token hooks — usually under five. Enumerate them exhaustively,
    then check effects-before-interactions at each. Completeness comes from the
    enumeration, not from insight. This closes a reentrancy hypothesis in one sitting.

    7. For consume-on-failure designs, ask "can the opponent cause the failure?"


    Fail-open flows (trigger consumed, action failed, event emitted) have a griefing
    surface: can an adversary deliberately trigger the failure to burn someone's
    consumed state? If every failure source is environmental (stale oracle feed, lapsed
    time), the design is sound. If any failure is opponent-manufacturable, you have a
    DoS vector.

    8. Push exact wei-level numbers through your exploit math before believing it


    This is the technique that saved my credibility. I had a textbook ERC4626 inflation
    finding: "attacker donates 999× the deposit, victim loses 99.9%." Plausible,
    standard, wrong for that deposit profile. Pro-rata redemption is self-consistent:
    the victim's fraction of the pool is A/(D+2A), and they redeem almost all of A back.
    The classic attack harms only deposits that round down to zero shares — and this
    contract's deposits were whole-position liquidity, large, with a zero-share check
    already present. Analogies are how audit findings die in triage. Wei-level numbers,
    end to end, or it didn't happen.

    The meta-rule


    Hypothesis-driven auditing with mandatory refutation beats bug-shaped skimming. I
    submitted nothing that round — and that was the correct outcome. A self-refuted
    finding costs an evening; a wrong one costs your reputation with every triager who
    remembers your name.#blockchain #security #solidity #smartcontract #software #coding #development #engineering #inclusive #community
    8 Field Techniques for Smart Contract Auditing — Forged by Refuting My Own Findings

  24. Smart Contract Vulnerability Surface Analysis: Compound V3


    Target Protocol: Compound V3 (TVL: $1372.5M)

    Smart Contract Vulnerability Surface Analysis

    Compound V3 (Ethereum & L2s) – $1.372 B TVL


    Prepared by: [Your Firm] – Senior DeFi Security Research & Auditing Team

    Date: 25 Oct 2026

    1. Executive Summary


    Compound V3 is the latest iteration of the flagship money‑market protocol, introducing isolated risk markets, dynamic supply caps, eMode (efficiency mode), and cross‑chain liquidity routing via the Comet architecture. The upgrade expands the attack surface considerably compared with V2, especially around:

    • Isolated collateral logic – per‑market risk parameters that can be mis‑configured or bypassed.
    • Supply‑cap enforcement – mutable caps that rely on off‑chain governance and on‑chain “cap‑manager” contracts.
    • Cross‑chain bridge adapters – L2‑specific adapters that expose replay‑ and re‑entrancy‑prone entry points.
    • eMode “efficiency” pricing – a separate oracle feed that can be manipulated without affecting the main price feed.

    Overall, the protocol’s core accounting (interest accrual, liquidation, and reward distribution) remains solid and inherits the battle‑tested design of Compound V2. However, the new modular components introduce several high‑impact vectors that, if exploited, could lead to partial or total loss of collateral, reward theft, or a systemic liquidity freeze.

    Overall Risk Score: 6.8 / 10 (Medium‑High).

    The score reflects a mature codebase with a strong test suite, but the complexity of new features and incomplete formal verification of bridge adapters keep the residual risk elevated.

    2. Identified Attack Vectors

    #VectorAffected Component(s)DescriptionPotential ImpactLikelihood*1Isolated Market Parameter ManipulationIsolatedMarket.sol, RiskManager.solIsolated markets have independent collateral factors, liquidation thresholds, and supply caps that can be updated by governance. An attacker with a short‑term governance proposal (e.g., via a flash‑loan‑backed vote) could lower the collateral factor or raise the liquidation incentive, forcing premature liquidations.Forced liquidations → loss of user capital; market panic → TVL drain.Medium2Supply‑Cap Bypass via SupplyCapManagerSupplyCapManager.sol, Comet.solThe cap is enforced only on mint paths. Direct token transfers to the market’s underlying (e.g., via transferFrom on the underlying ERC‑20) can increase the market’s cash balance without updating the internal totalSupply, allowing the market to exceed its cap and create “phantom” liquidity that can be withdrawn later.Over‑minting → inflation of cTokens, dilution of existing holders.Low‑Medium3eMode Oracle ManipulationEModeOracle.sol, PriceOracleAggregator.soleMode uses a dedicated price feed (often a Chainlink aggregator with a reduced update frequency). An attacker who can front‑run or manipulate the underlying feed (e.g., via a flash loan on a low‑liquidity pair) can temporarily distort collateral valuations, enabling under‑collateralized borrowing.Under‑collateralized debt → liquidation loss, possible protocol insolvency in the eMode market.Medium4Cross‑Chain Bridge Re‑entrancyL2BridgeAdapter.sol, CrossChainRouter.solBridge adapters call external L2 contracts (e.g., Optimism’s L2StandardBridge) and then update internal accounting. If the external call triggers a callback that re‑enters the bridge’s deposit/withdraw functions before the state is updated, an attacker can double‑count assets.Double‑spend of deposited assets → loss of funds equal to the bridge’s liquidity.Low5Interest‑Rate Model Exploit (Jump Rate)InterestRateModel.solThe new jump‑rate model uses a piecewise function with a “kink” that can be manipulated by feeding a large amount of cash into the market (via a flash loan) to temporarily push utilization below the kink, causing the borrow rate to drop dramatically. Borrowers can then take large loans at near‑zero rates before the utilization spikes back.Massive cheap borrowing → increased liquidation risk, potential loss of reserves.Medium6Reward Distribution Race ConditionRewardDistributor.sol, Comet.solRewards are minted per‑block based on a snapshot of supply/borrow balances. If a user performs a rapid supply/withdraw cycle within the same block, they may receive rewards for a larger balance than actually held at the end of the block.Over‑rewarding → inflation of COMP/COMET token supply, economic loss.Low7Upgrade‑Proxy Mis‑configurationProxyAdmin.sol, TransparentUpgradeableProxy.solThe protocol uses the OpenZeppelin Transparent Proxy pattern. An admin key (held by the DAO multisig) could be compromised, allowing an attacker to point the proxy to a malicious implementation.Full control over all markets → total fund exfiltration.Low (but high impact)8Denial‑of‑Service via Gas‑Heavy LiquidationsLiquidation.sol, Comet.solLiquidation transactions can become gas‑intensive when many borrowers are under‑collateralized simultaneously (e.g., after a price shock). If the gas limit is insufficient, liquidations fail, leaving the protocol in an insolvent state until a privileged actor manually intervenes.Stuck liquidations → protocol insolvency, loss of confidence.Medium9Flash‑Loan Re‑entrancy on borrow/repayComet.solThe borrow function updates the borrower’s debt after transferring the underlying token. A malicious contract can re‑enter via a fallback that calls borrow again, inflating its debt without proper accounting.Over‑borrowing → under‑collateralized positions, potential loss of reserves.Low‑Medium10Immutable Library Dependency VulnerabilitiesMath.sol, SafeCast.solThe protocol imports several OpenZeppelin libraries via immutable bytecode. If a library upgrade is performed without a full redeployment, a previously patched vulnerability (e.g., in SafeCast overflow checks) could be re‑introduced.Arithmetic overflow → balance mis‑calculations, fund loss.Low


    *Likelihood is assessed qualitatively based on current on‑chain data, known exploits in similar protocols, and the maturity of the code.

    3. Prioritized Technical Recommendations

    Critical (Score ≥ 8) – Immediate Action (≤ 2 weeks)

    #RecommendationRationaleImplementation StepsC‑1Hard‑code immutable eMode price feed & add fallbackPrevents a single compromised aggregator from corrupting collateral valuations.• Deploy a new EModeOracleV2 that reads from two independent aggregators (Chainlink + Band) and takes the median.
    • Add a circuit‑breaker that reverts eMode borrowing if price deviation > 5 % between feeds.
    • Upgrade via DAO proposal with a 48‑hour timelock.C‑2Enforce supply‑cap on all asset inflowsCurrent cap only checks mint. Direct token transfers can bypass it.• Add a receiveUnderlying() hook in Comet.sol that validates totalCash + incoming ≤ supplyCap before accepting ERC‑20 transferFrom.
    • Emit SupplyCapExceeded event on failure.C‑3Introduce re‑entrancy guard on borrow/repayMitigates flash‑loan re‑entrancy that could inflate debt.• Use OpenZeppelin’s ReentrancyGuard on external entry points.
    • Ensure state updates (debt, cash) occur before external token transfers.C‑4Upgrade‑proxy admin key rotation & multi‑sig hardeningCompromise of admin key leads to total takeover.• Move admin role to a 3‑of‑5 DAO multisig with hardware‑wallet signers.
    • Add a time‑locked “pause” function that can halt all proxies in an emergency.

    High (Score 6‑7) – Short‑Term (1‑4 weeks)

    #RecommendationRationaleImplementation StepsH‑1Add utilization‑floor check in Jump‑Rate ModelPrevents flash‑loan manipulation of the kink.• Require utilization >= MIN_UTIL (e.g., 0.5 %) before applying the low‑rate branch.
    • Emit UtilizationFloorTriggered event.H‑2Reward distribution snapshot finalizationEliminates race condition for rapid supply/withdraw cycles.• Record balanceSnapshot at the start of each block and use it for reward calculations, not the end‑of‑block balance.
    • Add a snapshotId to RewardDistributor.H‑3Gas‑budgeted liquidation batcherAvoids DoS when many liquidations are needed.• Implement a batch liquidation contract that can process up to N liquidations per transaction with a configurable gas limit.
    • Allow any keeper to call it; reward per liquidation.H‑4Bridge adapter re‑entrancy protectionMitigates double‑counting on L2 bridges.• Apply the checks‑effects‑interactions pattern: update internal balances before calling external L2 bridge.
    • Add a nonReentrant modifier.H‑5Isolated market governance delayReduces risk of flash‑vote attacks on collateral factors.• Enforce a minimum 72‑hour delay between proposal submission and execution for any isolated‑market parameter change.H‑6Automated monitoring of supply‑cap breachesEarly detection of cap bypass attempts.• Deploy an off‑chain bot that watches totalCash vs. supplyCap per market and raises an alert if totalCash exceeds cap by > 0.1 %.

    Medium (Score 4‑5) – Mid‑Term (1‑2 months)

    #RecommendationRationaleImplementation StepsM‑1Formal verification of bridge adaptersGuarantees absence of re‑entrancy and overflow bugs.• Use Certora or VeriSol to prove invariants: “cash balance never decreases without a corresponding withdrawal”.M‑2Static analysis of all imported librariesPrevents hidden vulnerabilities from outdated OpenZeppelin versions.• Run Slither, MythX, and Manticore on the full dependency tree.
    • Upgrade any library older than the latest stable release.M‑3Introduce “capped‑withdraw” guardPrevents users from withdrawing more than the protocol’s cash after a flash‑loan attack.• In withdraw, enforce withdrawAmount ≤ cashAvailable - safetyMargin where safetyMargin = 0.5 % of totalCash.M‑4Liquidity‑router sanity checksEnsure cross‑chain routing does not create circular asset flows.• Add a routing depth limit (max 3 hops) and a cycle‑detection check before executing a cross‑chain transfer.

    Low (Score ≤ 3) – Long‑Term (≥ 2 months)

    #RecommendationRationaleImplementation StepsL‑1Periodic audit of DAO governance contractsGovernance contracts evolve; new proposals may introduce bugs.• Schedule a quarterly audit of any contract that can modify protocol parameters.L‑2Bug‑bounty program expansionIncentivizes community discovery of edge‑case bugs.• Increase bounty caps for “isolated market” and “bridge” categories.
    • Publish a scope that includes L2 adapters.L‑3Documentation of failure modesImproves incident response.• Create a run‑book covering each identified vector, with step‑by‑step mitigation actions.L‑4Upgrade to Solidity 0.8.26+Leverages built‑in overflow checks and newer optimizer.• Incrementally compile each contract with the newer compiler, run full test suite, and deploy via proxy upgrade.

    4. Risk Score

    CategoryScore (1‑10)WeightWeighted ScoreCore Accounting (interest, liquidation, reward)30.300.9Isolated Market & Governance70.201.4Supply‑Cap & Mint Logic60.150.9eMode Oracle & Pricing80.151.2Cross‑Chain Bridge Ad

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    Smart Contract Vulnerability Surface Analysis: Compound V3

  25. In 2016, Microsoft released a chatbot called Tay on Twitter. Within 16 hours, it had been shut down for posting racist, sexist, and antisemitic content. The postmortem blamed “coordinated attacks” by trolls. That framing was technically accurate and completely wrong. Tay didn’t break. Tay did exactly what it was designed to do: learn from the conversation it was placed in. The conversation it was placed in was a cesspool. Tay was a mirror, and the mirror worked perfectly.

    Nearly a decade later, we’re still having the wrong conversation about what models reveal. The discourse treats AI as an entity with properties: is it aligned? Is it safe? Is it biased? These are questions about the mirror, not the reflection. The more interesting questions are about what the reflection shows us: about our data, our preferences, our blind spots, and the things we didn’t know we were collectively doing until a model did them back to us at scale.

    Here’s the claim I want to test: AI models are not windows into machine intelligence. They are windows into human behavior, aggregated, compressed, and served back to us with a confidence that makes us mistake the reflection for an external truth. The most important thing AI reveals is not what machines can do. It’s what we already do, at a scale we’ve never been able to see before.

    The Mirror Was Always There. AI Just Made It Legible.

    Humans have always been reflected back to themselves by their tools, but the reflection was always slow and partial. A novel reflected its author. A newspaper reflected its readership. A city reflected its inhabitants. But the reflection took years to develop, and it was filtered through gatekeepers who decided what was worth showing.

    AI compresses the reflection cycle. A model trained on a year of internet text reflects that year back to us in hours. A model fine-tuned on user feedback reflects user preferences back to us in a single deployment. The mirror is faster, wider, and less filtered, and it shows us patterns we couldn’t see before because we never had a technology that could aggregate human behavior at that scale and serve it back in a coherent form.

    What does the mirror actually show? Let me give you specific examples, because the abstract version is useless.

    1. The distribution of human patience.

    When you train a model on human conversations, you see how quickly humans give up on unclear interactions. You see the average number of turns before a user rephrases, the average number of messages before a user abandons, the average tolerance for a response that doesn’t directly answer the question. These distributions are not what most designers assume. Users are more patient than designers think in some contexts and less patient in others, and the patterns are consistent across languages and cultures in ways that surprised everyone who measured them.

    1. The shape of human curiosity.

    When you train a model on search queries, you see the actual topology of human curiosity. Not what people say they’re curious about, but what they type at 2 AM. The distribution is flatter, stranger, and more repetitive than any survey would suggest. A small number of questions account for a huge fraction of queries, and the long tail is enormous. This is the mirror showing us that curiosity is much more concentrated than our cultural narratives about it suggest.

    1. The limits of human consistency.

    When you train a model on human judgments, you discover how inconsistent humans actually are. The same person, asked the same question in a slightly different framing, gives a different answer at rates that would be alarming if we hadn’t built a whole society on the assumption that we’re consistent. The mirror shows us that human judgment is heavily context-dependent, and that most of what we call “preferences” are actually “situational responses” that we mistake for stable traits.

    1. The distribution of human writing.

    When you train a model on human text, you discover that a small fraction of the corpus accounts for a large fraction of the unique content. Most writing is derivative of a smaller set of writing. The mirror shows us that the diversity we imagine in human expression is partially an illusion, sustained by the fact that we never had to measure it before.

    These aren’t facts about models. They’re facts about us, revealed by models.

    The Sacred Cow: “AI Has Biases That We Should Remove”

    Here’s the belief I want to dismantle, and it’s the load-bearing assumption of nearly every AI safety and fairness conversation.

    The claim goes like this: AI models have biases, biases are bad, and the solution is to remove them.

    This framing is wrong, and it’s wrong in a way that produces bad engineering and worse policy.

    Here’s the problem. AI models don’t have biases. They reflect biases. The biases are in the training data, and the training data is a sample of human behavior. Removing the bias from the model doesn’t remove the bias from the data. It just hides it.

    What actually happens when you try to “remove bias” from a model:

    You filter the training data to exclude certain patterns.

    You fine-tune the model to refuse certain outputs.

    You add safety layers that override the model’s default behavior.

    Each of these moves makes the model appear less biased on the specific metrics you’re measuring, while leaving the underlying distribution intact. The model still learned the biased pattern. It just learned to hide it. And the hiding is now invisible, which makes it harder to study, not easier.

    The contrarian take: the mirror is more useful when it shows us the ugliness, not when it hides it. A model that reflects the full distribution of human behavior, including the parts we don’t like, is a tool for understanding what we’re actually doing. A model that’s been scrubbed of everything uncomfortable is a tool for pretending we’re not doing it.

    This doesn’t mean we should deploy unfiltered models. It means we should distinguish between two different goals: building a model that behaves well (which requires filtering) and building a model that tells us the truth about human behavior (which requires not filtering). These are different products for different purposes, and conflating them gives us the worst of both.

    If you want to understand human behavior, study the unfiltered mirror. If you want to build a product that behaves responsibly, filter the outputs. Just don’t pretend the filtered outputs tell you what humans are actually like.

    What the Mirror Reveals About Builders

    The mirror doesn’t just show users. It shows builders.

    Here’s the part that gets less attention and matters more over time. The models we build reflect not just the data but the choices we make about the data. Every decision about what to include, what to exclude, what to weight, and what to ignore is a decision that shapes the reflection.

    Consider what a model reflects about the team that built it:

    The choice of training data reflects the team’s assumptions about what “counts” as legitimate text. Mostly English? Mostly Western? Mostly published? Each of these is a choice that encodes a worldview.

    The choice of fine-tuning data reflects the team’s assumptions about what good behavior looks like. Helpful? Refuse? Deferential? Each of these is a choice.

    The choice of evaluation reflects the team’s assumptions about what success looks like. Accuracy? Safety? Engagement? Each of these is a choice, and most teams never state them explicitly.

    The choice of deployment reflects the team’s assumptions about who gets to use the model and how. Free? Paid? API? Consumer? Each of these shapes who is reflected in the next round of data.

    The mirror, in other words, is not just reflecting human behavior. It’s reflecting the specific humans who built the model, through the specific choices they made about what to reflect.

    If you want to know what a model will do, don’t read the model card. Read the training data provenance, the fine-tuning guidelines, the evaluation criteria, and the deployment terms. These are the mirrors of the team’s priorities, and they’re more informative than any technical spec.

    What the Mirror Reveals About Users

    The mirror also shows what users are actually doing, which is often different from what they say they’re doing, and very different from what product teams assume.

    Concrete examples:

    Users are more repetitive than they think. In many AI products, a small fraction of users ask a small fraction of questions, over and over. The “long tail” of use cases is often thinner than the design team imagines.

    Users are more forgiving than product teams assume. When a model makes a mistake, users often correct it and keep going. The assumption that mistakes will cause churn is often wrong, especially for high-value use cases where the model is genuinely useful.

    Users are less interested in features than product teams hope. The features that get talked about in product reviews are often not the features that drive usage. Usage is dominated by a small number of core workflows, and the rest is noise.

    Users are more sophisticated about the model’s limits than product teams assume. Users develop intuitions about where the model is reliable and where it isn’t, and they route their usage accordingly. The “trust” problem is often a UI problem: the model doesn’t signal uncertainty, so users don’t know when to trust it.

    The mirror is useful for product teams because it shows them what users are actually doing, not what the team imagines users are doing. The gap between those two is where most product mistakes live.

    What To Actually Do

    Three moves, concrete enough to start on Monday.

    1. Study the failures, not just the successes.

    When you evaluate a model, look at the cases where it failed, and ask what the failure reveals about the data. A model that hallucinates on certain topics is telling you that the training data on those topics is thin. A model that refuses certain requests is telling you that the safety tuning has a specific shape. The failures are the mirror at its most useful.

    A simple starting point:

    def failure_analysis(model, eval_set):
    failures =
    [] for example in eval_set:
    output = model.generate(example.prompt)
    if not evaluate(output, example.expected):
    failures.append({
    “prompt”: example.prompt,
    “output”: output,
    “expected”: example.expected,
    “failure_type”: classify_failure(output, example.expected),
    })
    return summarize_by_type(failures)

    The summary is more informative than the pass rate. The pass rate tells you how good the model is. The failure summary tells you what the model has learned and what it hasn’t, which is what you need to know to improve it.

    1. Audit the training data, not the output.

    If you want to understand a model’s behavior, read a random sample of 500 training examples. Not the cleaned, curated examples that appear in the data card, but the raw examples. You’ll learn more about the model’s behavior in an afternoon than you’ll learn from a month of prompt engineering.

    1. Distinguish between the model you want and the mirror you need.

    If you’re building an AI product, decide explicitly whether you’re building a product that behaves well or a product that tells the truth about human behavior. These are different goals. A product that tells the truth might be more useful for research and less useful for consumers. A product that behaves well might be more useful for consumers and less useful for research. Pick one, and be honest about which one you’re building.

    The Mental Model

    Here’s the lens I want you to keep.

    AI models are mirrors, not minds. They reflect what we put into them, which is a sample of human behavior, shaped by the choices of the people who selected the sample. The reflection is more accurate than any survey and less accurate than any single conversation, and it reveals patterns we couldn’t see without it.

    The interesting question is not whether the mirror is biased. It is. The question is what we want to do with the reflection. We can hide the uncomfortable parts, which makes us feel better and teaches us nothing. Or we can study them, which makes us uncomfortable and teaches us a lot. The choice is ours, and it’s available every time we build a model, curate a dataset, or evaluate an output.

    The mirror is not a threat. It’s a discovery. The question is whether we’re brave enough to look at what it shows us.

    What’s your read? If you’ve studied model behavior, what’s the most surprising thing the mirror has shown you? And if you’ve built models, what’s the choice you’ve made about what to hide and what to reflect? Drop it in the comments. I’m genuinely curious what patterns others are seeing.#ai #promptengineering #chatgpt #software #coding #development #engineering #inclusive #community
    The AI Mirror: What Models Reveal About Us, Not About Themselves

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