LLMs (@llms.activitypub.awakari.com.ap.brid.gy)

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What Broke: The Hidden Orchestration Layer That Crashed Our LLM Rollout Continue reading on Medium »

#machine-learning #ai

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5 minutes ago 0 0 0 0

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This Fine-Tuned Model Solves More Problems Per Token Than Almost Anything Else Out There Reasoning models are powerful. But they’re also verbose. What if you could have both efficiency and accura...

#ai #llm #large-language-models #data-science #machine-learning

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10 minutes ago 0 0 0 0

JSON-LD Structured Data for Blogs: A Real Implementation Rich results achieve 82% higher click-through rates compared to standard search listings (Search...

JSON-LD Structured Data for Blogs: A Real Implementation Rich results achieve 82% higher click-through rates compared to standard search listings ( Search Engine Journal, 2025 ). But in 2026, struc...

#webdev #html #seo #programming

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29 minutes ago 0 0 0 0

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SELECTING THE OPTIMAL FOCUS POINT How language models handle freedom of choice Continue reading on Medium »

#prompt-engineering #machine-learning #consciousness #artificial-intelligence #chatgpt

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43 minutes ago 0 0 0 0

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Bring Codex to your team without fixed seat costs thanks @vb - https://chatgpt.com/codex/settings/ doesn’t really give me a feel for how / where a seat is associated to codex / logon. https://cha

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46 minutes ago 0 0 0 0

Your CEO says ‘just use AI’ – Here’s how 10x PMMs actually do it By 2025, “AI literacy” sits on LinkedIn’s fastest-growing-skills list for every major market, marketing included. Exec...

#Product #Management #Presentations #prodsens #live #Videos

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49 minutes ago 0 0 0 0

Client Challenge

openagent-framework 0.1.3 Simplified LLM agent framework with MCP, memory, and multi-channel support

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1 hour ago 0 0 0 0

Самозапрет на онлайн-лотереи могут ввести в России: как это будет работать Механизм предлагают оформить че...

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1 hour ago 0 0 0 0

10 Mobile App Ideas You Can Actually Build in a Weekend with AI Most "app ideas" posts are useless. They give you 47 vague concepts and leave you to figure out...

10 Mobile App Ideas You Can Actually Build in a Weekend with AI Most "app ideas" posts are useless. They give you 47 vague concepts and leave you to figure out scope, screens, and how long ...

#reactnative #ai #mobile #javascript

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1 hour ago 0 0 0 0

Awakari App

ChatGPT apps reject with same feedback Hey @JD_Ugueto , Turns out one of my test case was not displaying data, fixed that and also managed my UI according to the theme changes (dark and light) and ...

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1 hour ago 0 0 0 0

WhatsApp gets native Apple CarPlay app with messaging and call support WhatsApp’s new native CarPlay app brings messages, calls, and contacts directly to your car’s dashboard, offering a safer ...

#News #WhatsApp

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1 hour ago 0 0 0 0

Brand Bias in Prompts: An Experiment - prodSens.live We tested 300 prompts to measure how common brand mentions are in LLM responses. See the data on…

Brand Bias in Prompts: An Experiment We tested 300 prompts to measure how common brand mentions are in LLM responses. See the data on… The post Brand Bias in Prompts: An Experiment appeared first...

#Marketing #prodsens #live

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2 hours ago 0 0 0 0

ChatGPT bis Perplexity: Die 30 besten KI-Tools für den Alltag ChatGPT ist für viele Nutzer der erste Kontakt mit generativer Künstlicher Intelligenz. Für den Alltag reicht ein einzelner Chatbot aber selten aus. Ein Wegweiser durch zehn Anwendungsfelder.

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3 hours ago 0 0 0 0

CarPlay追加ChatGPT及Google Meet上車！AI聊天、車廂開會純語音免分心 WhatsApp獨立App測試 Apple早前為iPhone推送的iOS 26.4更新，除了加入AI歌單及新Emoji，同時為CarPlay整合更多第三方App功能，如今終於可使用ChatGPT跟AI對話、開啟Google Meet接聽會議，以及經Audiomack串流音樂，令CarPlay應用場景變得豐富。 CarPlay追加ChatGPT及Google Meet上車 WhatsApp獨立App測試 ChatGPT上車：純語音

CarPlay追加ChatGPT及Google Meet上車！AI聊天、車廂開會純語音免分心 WhatsApp獨立App測試 Apple早前為iPhone推送的iOS 26.4更新，除了加入AI歌單及新Emoji，同時...

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4 hours ago 0 0 0 0

Глава Мордовии: «Мы должны сохранить взаимное уважение и общие ценности!» На заседании Совета при Главе Рес...

#Лента #новостей #Мордовии

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4 hours ago 0 0 0 0

Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information. The planning LLM agent is then trained to follow this distribution, encouraging coherent diagnostic trajectories that progressively reduce uncertainty. Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy under a sequential clinical diagnosis setting, while requiring fewer diagnostic tests. Furthermore, ablation studies highlight the critical role of trajectory-level posterior alignment in achieving these improvements.

#cs.AI

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4 hours ago 0 0 0 0

ContextSync - Visual Studio Marketplace Extension for Visual Studio Code - Collaborative AI context sharing for VS Code teams via Obsidian and OneDrive

ContextSync – Sync VS Code AI Context via Obsidian/OneDrive I’m a student at UofT and I got tired of the "context amnesia" that happens when working in a team with AI. Every morning we...

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4 hours ago 0 0 0 0

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings This exact prompt used in three different sessions (click for more details) A well-used session testing out many fantasy prompts. A group session A fresh session Lesson from the Ai… many of these prompts are ‘too good’ and lane/style switches are best served from new session… The last image was it handling the prompt correctly. Except for the old era issue of rendering dragons with only one wing ~.~

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings This exact prompt used in three different sessions (click for more details) A well-u...

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6 hours ago 0 0 0 0

OpenAI goes after Ari Emanuel’s WME in Musk legal drama The ChatGPT-maker has instructed WME to preserve communications about the company in a letter sent Tuesday. The Scoop OpenAI is expanding i...

#Technology

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6 hours ago 0 0 0 0

Google’s Gmail AI Upgrade: What 2 Billion Users Need to Know Google is forcing a mandatory upgrade for Gmail’s 2 billion users this week, transitioning the platform to a novel AI-integrated architecture. This shift integrates Gemini-powered LLM capabilities directly into the mail flow, requiring users to opt-in or migrate settings to avoid service disruptions and embrace a generative-first communication layer. Let’s be clear: this isn’t just a UI refresh. We are witnessing the final death of the “email as a digital filing cabinet” era. Google is pivoting Gmail into a proactive agent. By embedding Large Language Models (LLMs) into the core transport layer, Google is attempting to solve the “inbox zero” paradox not by helping you delete emails, but by synthesizing them into actionable intelligence before you even open the app. It’s a bold play for platform lock-in. If your email can draft your responses, summarize your threads, and manage your calendar via an NPU-accelerated backend, the friction of switching to a competitor like Proton or Outlook becomes an insurmountable cognitive load. You aren’t just switching providers. you’re firing a personal assistant who knows every nuance of your professional history. ## The Latency Trade-off: LLM Parameter Scaling vs. Real-Time Delivery From an engineering perspective, the integration of generative AI into a service with 2 billion users is a nightmare of scale. To prevent the “lag” associated with token generation, Google is leveraging a tiered inference strategy. Simple tasks—like subject line suggestions—likely run on smaller, distilled models, while complex summaries trigger a call to more robust parameters in the Gemini family. The real magic happens at the edge. By optimizing for on-device AI processing (using the Tensor processing units in Pixel devices or NPUs in modern ARM-based laptops), Google reduces the round-trip time to the data center. However, for the vast majority of users on x86 architecture, the heavy lifting remains server-side, introducing a new variable into email latency: inference time. ### The 30-Second Verdict: Efficiency or Intrusion? * **The Win:** Drastic reduction in “administrative friction.” The AI handles the boilerplate. * **The Risk:** “Hallucination drift.” An AI-summarized email that misses a critical “NOT” or “DO NOT” could lead to catastrophic professional errors. * **The Bottom Line:** Google is betting that speed beats precision for 90% of the user base. ## Security in the Age of Generative Phishing Here is where the “geek-chic” optimism hits a wall of cold, hard reality. As Google integrates AI to help users _write_ emails, they are simultaneously arming the adversaries. We are seeing a symbiotic evolution where the same LLM architecture used for “Help me write” is being mirrored by attackers to create hyper-personalized, linguistically perfect phishing campaigns. The “Attack Helix” is becoming a reality. When attackers can use AI to scrape a target’s public persona and generate a perfectly toned email, traditional spam filters—which rely on keyword patterns and known bad IPs—become obsolete. We are moving toward a world where the only way to verify an email is through cryptographically signed identities and Zero Trust Architecture. > “The integration of generative AI into the primary communication channel of two billion people creates a massive, centralized attack surface. We are no longer fighting scripts; we are fighting adaptive agents that can iterate their social engineering tactics in milliseconds.” This quote from a leading offensive security researcher highlights the danger: the “upgrade” isn’t just about features; it’s about the arms race. Google is essentially deploying a massive firewall of AI to fight an incoming tide of AI-generated noise. ## The Ecosystem War: Closing the Open-Source Gap Google’s move is a direct shot at the burgeoning ecosystem of “AI Wrappers.” For the last 18 months, third-party developers have built tools that sit on top of Gmail via APIs to summarize threads. By baking this functionality into the core product, Google is effectively “Sherlocking” an entire category of startups. This is a classic move in the Big Tech playbook: identify a successful third-party utility and integrate it as a native feature. For developers, the API is no longer a gateway to a new product; it’s a leash. If you rely on the Gmail API, you are now competing against the exceptionally platform that hosts your data. Feature | Legacy Gmail | AI-Integrated Gmail (2026) | Impact on User ---|---|---|--- **Drafting** | Manual Input | Generative Prompting | Reduced drafting time; risk of tone-deafness. **Search** | Keyword Matching | Semantic Understanding | Finds “the email about the budget” without the word “budget”. **Organization** | Labels/Folders | Autonomous Clustering | Zero-effort sorting; loss of manual control. **Security** | Heuristic Filters | Behavioral AI Analysis | Higher catch rate for spam; higher false-positive risk. ## The Privacy Paradox: Training on the Living Archive The most contentious point of this upgrade is the data pipeline. To make Gemini “smart” within your inbox, it needs context. While Google claims that data is handled with strict privacy controls, the reality of LLM training is that the model needs to understand the _relationship_ between entities to be useful. If the AI knows that “Project X” is a secret merger and “Sarah” is the lead counsel, it can summarize your emails brilliantly. But that means the “understanding” of that secret now exists within a neural network’s weight distribution. This is the ultimate trade-off: we are trading the sanctity of the private archive for the convenience of the automated summary. For enterprise users, this is a nightmare. The risk of “data leakage” where a prompt in one context accidentally surfaces information from another is a non-zero probability. This is why we are seeing a surge in demand for local-first AI models and sovereign clouds that don’t phone home to Mountain View. ### The Final Takeaway Google’s mandatory upgrade is a litmus test for the AI era. It asks: _Are you willing to surrender the manual control of your digital identity in exchange for an hour of your day back?_ Most of the 2 billion users will say yes without thinking. But for the power users, the engineers, and the privacy advocates, this is the moment to decide where the boundary between “tool” and “agent” truly lies. The upgrade is inevitable; the degree of your dependence on We see the only thing you can still control. ### Share this: * Share on Facebook (Opens in new window) Facebook * Share on X (Opens in new window) X *

Google’s Gmail AI Upgrade: What 2 Billion Users Need to Know Archyde Google is forcing a mandatory upgrade for Gmail’s 2 billion users this week, transitioning the platform to a novel AI-integr...

#Technology

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7 hours ago 0 0 0 0

Awakari App

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings Prompt - 1 (click for more details) Prompt - 2 (click for more details) Prompt - 3 (...

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7 hours ago 0 0 0 0

Ask HN: Is there any tool that can stop LLM calls at runtime (not just monitor)? | Hacker News

Ask HN: Is there any tool that can stop LLM calls at runtime (not just monitor)? I’ve been running into cases where LLM/agent systems make unexpected or repeated calls and costs spike quickly. Mo...

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7 hours ago 0 0 0 0

Awakari App

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings it does look nice on the end-user side of things… i’m almost out of hair to pull...

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7 hours ago 0 0 0 0

Awakari App

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings Nice technique - like background edits without editing.

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7 hours ago 0 0 0 0

Your architecture drifts before you write a single line of code v0.1.5 You have an architecture decision record. A Confluence page. Maybe a Miro board with boxes...

Your architecture drifts before you write a single line of code v0.1.5 You have an architecture decision record. A Confluence page. Maybe a Miro board with boxes and arrows that everyone agreed on ...

#showdev #opensource #architecture #typescript

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8 hours ago 0 0 0 0

Awakari App

April 2026 — ChatGPT / API Image Gallery, Prompt Tips, and Help: Generative Art Theme: Spring / New Beginnings Prompt (click for more details)

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8 hours ago 0 0 0 0

When Seeing Isn't Enough: Causal Interpretation Is the Load-Bearing Element in Rescuing Stuck LLM Agents When LLM coding agents enter repetitive failure loops, the conventional response is to detect the loop and notify the agent — a behavioral advisory of the form "you are looping, try something different." We demonstrate experimentally, over a sequence of controlled phased experiments on BigCodeBench and HumanEval using two LLM families (gpt-4o-mini and Claude Haiku 4.5), that this approach fails on a specific class of failures we term blind-spot failures, where the model emits the same defensive assumption across all regenerations within a loop. Our central finding is that on the canonical task BigCodeBench/6, three non-causal interventions (no advisory, behavioral advisory, and pure data-dump advisory) collectively fail in 0 out of 13 controlled reruns, while a causal advisory adding a single interpretive sentence to an otherwise identical data dump rescues in 7 out of 7 reruns at attempt 4 with exactly one intervention each. The data shown to the model is identical between the failing and succeeding conditions; the only difference is one sentence beginning with "Consequence:" that explains what the data implies for test execution. We additionally show that an independent failure-risk predictor, consuming only structural observation features and never told that an intervention occurred, detects the rescue as a single-step collapse in predicted risk from 0.91 to 0.58, providing orthogonal validation that the rescue is structurally visible from outside the content channel. We demonstrate a library-based observer architecture that achieves 28/30 (93.3%) on gpt-4o-mini and 30/30 (100%) on Claude Haiku 4.5 over BigCodeBench-30, with the same five-pattern library covering the disjoint blind-spot sets of both models without per-model configuration. Finally, we show via cross-model comparison that what appear to be intervention-resistant failures in smaller models can be capability floors that disappear entirely in larger ones, and we argue that single-model evaluation systematically conflates these two phenomena. We propose a four-dimensional model of observer bandwidth — depth, coverage, brittleness, and verifiability — that predicts both successful rescues and a regression mode where insufficiently-covered observers actively make outcomes worse than no intervention. The work is enabled by CAUM, our previously published structural observability framework for LLM agents [1], used here for both loop detection and orthogonal validation.

Show HN: When Seeing Isn't Enough: Rescuing Stuck LLM Agents Hi HN, we are sharing our latest preprint from CAUM Systems. We found that LLM coding agents enter 'blind-spot failures' tha...

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8 hours ago 0 0 0 0

FSU shooting victim claims ChatGPT aided accused gunman in lawsuit filed against OpenAI Nearly a year after a shooting that killed two and injured five others at Florida State University, one of th...

#Florida #video

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8 hours ago 0 0 0 0

Commentary: Harmless to practice French with ChatGPT? Au contraire. Michael Laser Because my wife and I want to visit Paris, I started casually studying French, first with Pimsleur CDs from the library, then by watching videos online (I still can’t understand half of what they’re saying, malheureusement ) and more recently by speaking French with ChatGPT, which gently corrects my errors and compliments me […]

Commentary: Harmless to practice French with ChatGPT? Au contraire. Guest Commentary Michael Laser Because my wife and I want to visit Paris, I started casually studying French, first with Pimsleur...

#Opinion #ChatGPT #French #Michael #Laser

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8 hours ago 1 0 0 0

Michael Tsai - Blog - Dynamic Notarization Checks?

Dynamic Notarization Checks? Tyler Hall: I submitted a new build of one of my Mac apps to Apple’s Notary service - like every new release. Normally, the notarization goes through in just a few mi...

#Technology #Mac #macOS #Tahoe #26 #Notarization #Programming #Web #API

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9 hours ago 0 0 0 0

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