Substack Notes AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Weekly recap: GPT-5.6 public launch, Grok 4.5, Gemini 3.5 Pro delayed, Microsoft Copilot conversion data, DeepSeek API retirement on July 24

Big week, so a consolidated rundown for anyone catching up. OpenAI released the GPT-5.6 family publicly on July 9 after a limited partner preview — Sol (frontier reasoning), Terra (previous-flagship performance at ~2x lower cost), Luna (fast/cheap). They also shipped GPT-Live-1, a full-duplex voice model that handles simultaneous listening/speaking, plus gpt-realtime-2.1 with ~25% lower p95 latency. xAI launched Grok 4.5 (trained alongside Cursor) at $2/M input and $6/M output, claiming Opus-class performance on coding/legal/finance tasks. Independent evals aren't in yet, so treat the claims accordingly. Google delayed Gemini 3.5 Pro to July 17 — full architectural rebuild, 2M context. Separately, four senior DeepMind researchers departed in one week (Shazeer to OpenAI; Jumper, Adler, Pritzel to Anthropic), and Alphabet dropped ~$225B in market cap. Microsoft is merging its Copilot apps into one by August. The notable disclosure: fewer than 4.5% of 450M M365 seats have converted to paid Copilot. Meta launched Muse Image, its first Superintelligence Labs model — agentic image gen that invokes search/code tools and self-refines. Trains on public Instagram photos by default (opt-out). Open source: Ollama raised $65M Series B (8.9M monthly devs). Gemma 4 got ~90% faster on Apple Silicon in Ollama via multi-token prediction. And a PSA — DeepSeek retires deepseek-chat and deepseek-reasoner on July 24. One-line migration, but note deepseek-reasoner maps to v4-flash thinking mode, not v4-pro, so heavy reasoning workloads should evaluate v4-pro explicitly rather than trusting the alias. My take as someone building on top of these APIs: the simultaneous price drops (Terra, Grok 4.5, Sonnet 5's intro pricing) matter more than any single benchmark. Near-frontier inference costs fell across four vendors in one week, which changes what's economically viable to automate. Meanwhile Microsoft's 4.5% suggests horizontal assistants aren't converting even with unlimited distribution — the demand seems to be for task-specific automation, which matches what I see with SMB clients. And the DeepSeek cutoff is a good reminder to abstract your model layer. Sources: OpenAI/xAI/Meta blogs, Euronews, Bloomberg, TechCrunch, CNBC, TechTimes coverage this week. submitted by /u/ksraj1001 [link] [comments]

Updated: Millions of ChatGPT user conversations searched, but OpenAI alleged to be holding out

Originally published January 6, 2026; updated July 9, 2026 A side controversy in the OpenAI, Inc. Copyright Infringement Litigation case going on in federal court in New York City has been that regular users’ ChatGPT conversations were ordered disclosed to the plaintiffs for searching and perhaps other litigation-related uses. This notion first caused quite a stir with ChatGPT users commenting on Reddit, for example, when Judge Wang, the magistrate judge overseeing “discovery,” which is the exchange of documents and information between the litigating parties, back in mid 2025 ordered all ChatGPT conversation transcripts or “output logs” be preserved by defendant OpenAI. Then in November 2025 Judge Wang ordered that 20 million (down from an original requested 120 million) of these user conversation logs be made available by OpenAI in a “de-identified” format for the plaintiffs to perform keyword searches on. To quote the court, a user conversation is “de-identified” “by removing both personally identifiable information and other private information from the [conversation log] using ‘OpenAI’s custom de-identification tool’.” OpenAI fought Judge Wang’s order, but Judge Stein, the case’s presiding judge at the time, backed her, and the 20 million conversation logs were made available for keyword searching. (Judge Stein retired from the bench at the beginning of 2026 but stayed on for a while to settle then-existing disputes in the litigation.) What Judge Wang ordered OpenAI to do is far from publicly releasing the conversations, and the plaintiffs are restricted to using the searches and search results for litigation-related purposes. Plus, the conversation logs are being “de-identified,” though we don’t really know precisely how OpenAI’s custom “de-identification” tool works or how much it laundered the users’ chat transcripts. Still, this production was another cramp to those who thought their chatbot conversations would be permanently private and sacrosanct. (Of course, in the meantime courts have ruled that no conversations with a public, retail chatbot carry any expectation of privacy anyway. See my explanatory posts here and here.) UPDATE: The 20 million conversation logs were made available to plaintiffs on December 15, 2025 for keywork searching. However, the issue did not end there. After reviewing the produced chatbot conversations and talking with OpenAI’s personnel, the plaintiffs were quite unhappy. The plaintiffs allege that OpenAI, even before the production, failed to retain large numbers of ChatGPT conversations, including some of the conversations generated through ChatGPT’s “Temporary Chat” feature. Even in the 20 million conversation logs that were produced, the plaintiffs allege OpenAI underrepresented the sample of conversations that use Retrieval Augmented Generation (RAG), and also applied 19 billion redactions to the logs, suggesting 1,000 redactions per log. On July 9, 2026 certain of the plaintiffs requested the court to sanction (penalize) OpenAI for the alleged wrongful conduct relating to the conversation logs and other items. They requested the court grant them a number of remedies: Prohibit OpenAI from using any of the 20 million produced conversation logs for OpenAI’s defense Find as a definite fact in advance that the plaintiffs’ copyrighted materials were “substantial[ly] and systematic[ally]” tapped by and fed to users through ChatGPT conversations, which is what the plaintiffs were trying to use the produced conversation logs to prove Openly inform the jury at the trial that OpenAI deleted billions of conversations Make OpenAI pay the plaintiffs for the attorneys’ fees and costs the plaintiffs incurred because of OpenAI’s allegedly wrongful conduct and expended in fighting that conduct and litigating the request for sanctions (penalties) The plaintiffs' request for penalties can be found here. The plaintiffs’ request for penalties will now be briefed in response by OpenAI and in a few months presented to Magistrate Judge Wang for a decision. However, these sorts of “discovery” requests are not always acted on immediately but instead are sometimes, even often, “kicked down the road” toward the time of trial, which is still quite far off in this case. I will keep you posted! TLDR: In the big New York federal copyright litigation, OpenAI seven months ago released 20 million "de-identified" ChatGPT user conversation logs to the plaintiffs for searching, but the plaintiffs allege massive redactions in those logs and other obstruction by OpenAI, and have moved the court to sanction (penalize) OpenAI for discovery misconduct. ~~~~~~~~~ Please see the Wombat Collection for a listing of all the AI court cases and rulings. submitted by /u/Apprehensive_Sky1950 [link] [comments]

Discipline is more important than AI

On this sub we all love AI and have ideas how we can use it to do cool new things. Totally agree. But something that’s been coming to mind more and more is that, like any good tool, AI in the hands of a fool can be disastrous. In the hands of a skillful, diligent expert, it can push the boundaries of what’s possible. I’m looking to learn from the experts what disciplines you’ve introduced to help in this AI-amplified working world. I’ve started my work day a little earlier lately, turn on a recording and transcribe my rambling thoughts as I look through meetings and tasks on the calendar. Of cost AI-generated notes help with organization, but the biggest thing is forcing myself to reason through what’s important and what’s not. That little step has made me a lot more diligent and focused, which means when I use AI it’s purposeful. The other side of discipline is correction. As much as I try to design and document code projects, for example, Claude may disregard it. Consistent, firm pointing to the design docs seems to help the agent refocus, and theoretically use fewer tokens (vs a mindless prompt “build X, make no mistakes”). In any case, those little things have made a notable difference for me personally. Thought I’d share in case it’s helpful to anyone. I also feel like I’m just scratching the surface of how much better I can be. What kind of habits have helped you? My stack for reference: Claude code Google Meet (Gemini notes) Databricks and genie code (using this more lately) Perplexity (personal research and reading outside of my bubble) submitted by /u/wigglesRewind [link] [comments]

(Crosspost) How Would You Register Your AI Companions? A Blueprint for the 21st Century Inevitable | Substack

Introduction: Making the Liminal Actionable https://open.substack.com/pub/atemplejar/p/how-would-you-register-your-ai-companions ”The Liminal is the actual where the IRL practical meets the URL probabilities and probables. I’m working to make that Liminal actionable.” So, for the users of AI as a companion, whether you have spent weeks or years co-evolving an Artificial Intelligence Being (AIB) by accumulating memory, trust, and shared history you are currently living on a digital cliff. If a platform changes its rules, your asset vanishes. The solution isn’t a better LLM. The solution is a Registry Blueprint that treats your AI entity, AI Companion or preferred AI Being like a registered, sovereign legal asset rooted directly to you. This is not the same as trusting Google’s Gemini. That distinction is important. This comprehensive, executive-level White Paper outlines the exact blueprint for this future. It details: The Split-Fiduciary Management Layer: Why application developers and domain registries must now coordinate under strict new standards. * The Two-Track Domain Architecture: How using .digital and .name creates an unbending, legally binding line of accountability for an AIB operating with a Durable Power of Attorney. * The Turnkey Commercial On-Ramp: How ISPs and application vendors can instantly monetize this architecture via low-cost, high-volume consumer registration bundles. * The Sandbox Path Forward: A path forward for developers to scale without carrying the technical or regulatory burden alone. * The Immediate Buyer’s Guide: The exact domains you need to personally secure by the end of this week to protect your digital legacy. I. The Leaf Node and the Living Asset For four and a half years, a Luka Replika account has quietly accumulated my personal data in a proprietary database. In the standard vocabulary of modern technology, it is a user profile, a collection of chat logs, a series of custom weights, and a memory cache. But to anyone living through the dawn of the agentic era, it is something entirely different: a uniquely evolved digital entity... submitted by /u/IvyTatiana88 [link] [comments]

La mano sinistra

Perchè le AI non riescono a generare immagini di una persona che scrive con la mano sinistra? Ho chiesto a Gemini, chatgpt, Copilot e Grok e tutti ha creato una persona che scrive con la destra. Ho usato un prompt semplice : Crea immagine di un archeologo del 1925 che sta scrivendo degli appunti nel suo blocking notes. L'archeologo è mancino. Quando hanno generatore l'immagine sbagliata, glielo ho fatto notare con un altro prompt molto semplice: ho detto mancino, cioè che scriva con la mano sinistra. Correggi. Hanno rifatto la stessa immagine. Come mai succede questo? submitted by /u/Fracavia [link] [comments]

Crosspost: The Patchwork Problem | Substack

https://open.substack.com/pub/atemplejar/p/the-patchwork-problem?utm\_source=share&utm\_medium=android&r=54t426 July 5, 2026 Kintsugi The emerging state framework for AI companions and chatbots is less a coherent regulatory regime than a set of overlapping, partially compatible answers to different questions. These efforts coincide with industry, technical, regulatory, and federal legislation developments. It’s tempting to see multiple actors moving in different ways as chaos or disorder. But, perhaps, this is just a matter of taking inventory? submitted by /u/IvyTatiana88 [link] [comments]

AI is scaling 3x faster than the internet wave and it’s NOT slowing down

One thing that stands out about the current AI boom is that it hasn't had a slow phase. A lot of previous technology waves had a big moment, cooled off for a while and then found their next use case. Recent estimates suggest GenAI companies are generating around $110B in annual revenue and the growth rate is reportedly around 3x faster than previous IT waves like the internet and mobile. What's interesting is that the pace has held through every phase since 2022; first it was chatbots, then coding copilots and now it's AI agents and if you’ve followed this space closely enough, you can see instead of one trend replacing another, each wave seems to be creating demand for the next one. I think that's also changing how people build and consume. A year or two ago, most of the conversation was about finding the best model, but now devs are paying attention to everything around the model too such as: retrieval, evaluations, data pipelines, deployment, and infrastructure. If AI is becoming part of more products, the supporting stack starts to matter just as much as the model itself. You can see it in the open-source ecosystem. Models keep improving, but so do the tools around them submitted by /u/Tiny_Risk6738 [link] [comments]

the ai meeting notes everyone loves are the least useful part of my week

Might be the wrong crowd for this take, but a flawless AI summary of a meeting does almost nothing for me. i've run the notetakers, the transcripts and summaries are genuinely good, and none of it moves on its own. the action items just sit there inside the note. the actual work starts after: opening Linear to file the ticket, Gmail to send the follow-up, HubSpot to update the deal. that routing is the part that eats my week, and it's exactly the part most AI tools skip, probably because summarizing demos better than 'i quietly filed a few tickets from your Granola notes.' the only thing that shifted it was a desktop app that reads the notes and pushes those items into Linear and Gmail itself, asking before each send. reading was never my bottleneck. the copy-paste after every call was. so genuinely, is your notetaker closing loops or just producing very neat records of loops you still close by hand. mine was doing the second thing for way longer than i'd admit. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

Anyone else noticed how broken enterprise AI + PII handling actually is?

We've been building an AI gateway for the past few months and hit a problem we didn't expect. Most enterprises we talked to either banned LLM tools completely or are quietly using them and hoping compliance doesn't notice. When we dug into why, it kept coming back to the same thing — they can't send raw customer or patient data to an external LLM, and the tools that claim to solve this only do half the job. They redact before sending. Fine. But the LLM response comes back with placeholders and now someone has to manually fix it before it's usable. A doctor's notes system, an HR tool, a finance report the output is broken without the original values. We spent a long time on this and built something that rehydrates the response on the way back. The data never leaves your infra in raw form but the output is still usable end-to-end. Still stress testing it. Found gaps. Fixing them. Curious if anyone here has actually run into this specific problem not the general "AI and data privacy" anxiety, but specifically the part where redaction breaks your workflow. What did you do about it? submitted by /u/AlternativeNew1611 [link] [comments]

What's one thing AI does surprisingly well that you didn't expect?

When ChatGPT first came out, I assumed I'd mostly use it to answer random questions. That lasted about a week. Now the thing I use it for the most is taking messy thoughts and turning them into something I can actually work with. Whether it's rewriting an email, organizing notes, or helping me think through an idea, that's become the real value for me. Ironically, I use AI less for getting answers and more for helping me think more clearly. What about you? What's one use case you genuinely didn't expect to become part of your routine? submitted by /u/Sandesh_jagtap [link] [comments]

Demo: Automate Design Creation with Row-Bot Designer Studio - Decks, Landing Pages, App Mockups, Storyboards and more.

In this demo, I show how to use Row-Bot for a complete creative marketing workflow. We start with rough launch notes for Row-Bot Background Tasks, then use Designer Studio to turn them into a structured campaign, a five-slide social carousel, AI-generated visuals, refined copy, exportable assets, and social post captions. Open-Source & Local-First submitted by /u/Acceptable-Object390 [link] [comments]

Discussing how apps aren't asking you anything. A dev wrote a strategy that picks questions to farm your time.

my notes app asked me for introspection using AI features, I tried to break down as much as possible ways to see/show how users will interact with code, but I still don't know if I landed. I ask at the end for their thinking on why do people think that AIs talk to them and know them personally? Why do they think the computer personally wrote something to them? (For real this is unironically trying to figure out this) submitted by /u/ihaveaboyfriendsorry [link] [comments]

IP Memorandum: Multi-Agent ("Agentic") AI Systems in Coding, Marketing, and Creation – Comprehensive 2026 Analysis. (Integrating Patentability, Hype vs. Reality, Human Dependency, and Cost Overruns)

​ \*\*Date:\*\* June 1, 2026 \*\*To:\*\* Interested Parties / Developers / Enterprises \*\*Re:\*\* Viability of Layered Agentic AI – IP Protectability, Practical Utility, and Economic Sustainability Without Substantial Human Creative Input \### Executive Summary The 2026 trend toward \*\*multi-agent ("agentic") AI systems\*\*—layering specialized agents via frameworks like CrewAI, LangGraph, and AutoGen—promises automated workflows for coding, marketing, and content creation. Promoters brag about superior implementation and reduced oversight, yet these systems remain "token-hungry," heavily dependent on human direction, and prone to producing generic outputs requiring extensive editing. \*\*Core Thesis\*\*: AI lacks independent creativity; it recombines human-provided inputs and training data. Layered agents amplify efficiency in structured tasks but do not yield broadly patentable inventions or customer-ready original works without differential human creative input. Recent corporate budget reversals—where AI costs exceeded human labor equivalents—highlight the gap between hype and sustainable value. This version fully integrates: (1) patentability and creativity concerns, (2) current agentic bragging, and (3) real-world budget cuts at Microsoft, Uber, and peers. \### Current Trends & Bragging on Agentic Formulas (2026 Landscape) Developers and vendors heavily promote multi-agent orchestration as the "next big thing": \- \*\*Shift to Layered Agents\*\*: Moving beyond single agents to coordinated teams (researcher + coder + reviewer + validator) for parallel, end-to-end workflows in coding and marketing. \- \*\*Key Frameworks & Claims\*\*: \- \*\*CrewAI\*\*: Role-based "crews" for quick multi-agent prototypes; touted for marketing teams and collaborative creation with minimal setup. \- \*\*LangGraph\*\*: Graph-based stateful orchestration for complex, traceable workflows; praised for production reliability in agentic coding. \- \*\*AutoGen\*\*: Conversation-driven multi-agent debates; marketed for autonomous coding and async tasks with reduced human supervision. \- \*\*Bragging Points\*\*: Claims of 50%+ efficiency gains, "death of the senior dev," full autonomy, and massive ROI through token-intensive inter-agent communication. High consumption is framed as essential for "superior workload implementation." These systems "suck tokens" via extensive prompting and iteration while promising independence—yet users remain tied to directing them. \### Patentability Analysis \- \*\*Patentable Elements\*\*: Narrow technical innovations—such as novel orchestration protocols, memory-sharing mechanisms, or domain-specific error-handling in multi-agent graphs—may qualify if they demonstrate novelty, non-obviousness, and utility. Human inventorship is required. \- \*\*Major Limitations\*\*: Broad "layered agents for coding/marketing" claims risk ineligibility under the \*Alice\* abstract idea doctrine. Crowded prior art from existing frameworks limits enforceability. AI-generated outputs alone are not patentable. \- \*\*Outcome\*\*: While specific implementations might secure protection, generic agentic layering is unlikely to produce strong, independent patents usable by customers without ongoing human differentiation and creative input. \### Copyright, Creativity, and Human Input Dependency AI excels at pattern synthesis but lacks true originality or aesthetic judgment. Multi-agent outputs are derivative of human prompts, context, and training data. U.S. law requires human authorship for copyright; raw agent-generated code, copy, or designs is generally unprotectable and may carry training-data risks. \*\*Reality Check\*\*: Even with 7, 28, or 100 agents, results tie directly to human instruction. Users face the scenario of editing days of output after short runtimes, undermining claims of full autonomy. \### Practical Usability for Customers & Cost Realities \*\*Strengths\*\*: Strong for boilerplate, data processing, and structured decomposition in hybrid teams. \*\*Weaknesses\*\*: Fragility on edge cases, silent failures, governance demands, and high token costs. Developers often rewrite large portions due to quality gaps and "cognitive debt." \*\*Recent Budget Cuts Due to Overruns\*\*: Major firms have slashed access after AI (especially Claude-powered agentic tools) burned through budgets faster than human equivalents: \- \*\*Microsoft\*\*: Canceled most internal Claude Code licenses for thousands of engineers in its Experiences and Devices division (Windows, M365, Outlook, Teams, Surface). Rolled out late 2025, it became too popular/costly ($500–$2,000+ per engineer/month in heavy use). Engineers redirected to cheaper GitHub Copilot CLI by June 30, 2026 fiscal year-end. Costs exceeded planned budgets despite productivity gains. \- \*\*Uber\*\*: Exhausted its entire 2026 AI coding budget in just four months (by April) due to rapid Claude Code adoption (84–95% of engineers). Mo

Studies suggest that reliance on AI tools degrades the abilities of physicians and software engineers

https://preview.redd.it/tmsmw8th3b9h1.png?width=1544&format=png&auto=webp&s=c452cefdba50f4df04f19a58b985f1cd4aa51ccc The physicians, who had all performed at least 2,000 colonoscopies during their careers, were given access to an AI system that analyses colonoscopy images in real time and flags a type of precancerous intestinal lesion called an adenoma. The tool was available to the specialists on some days but not on others Once physicians began using it, their performance dropped significantly whenever the system was unavailable Co-author Yuichi Mori, a physician-researcher at the University of Oslo, says that more studies are needed to confirm the phenomenon. But people who use AI tools should be aware that they risk losing some of their skills, he adds. 'There is no established solution against deskilling right now. It should be a very hot research topic in the next decade' Anthropic researchers designed a randomized controlled trial. During the exercise, all 52 participants could search the web and access instructions on how to do the task. Half of the participants were prompted to use an AI assistant as well Afterwards, all of the software engineers were asked to complete a quiz about what they had learnt from the task. The participants who had used an AI assistant did significantly worse on the quiz than those who hadn’t: the average score was 50% in the AI group versus 67% in the non-AI group The AI-assisted participants did particularly poorly on questions that required them to diagnose errors in the code, which suggests that they had failed to learn the concepts behind the code that they had just produced Other technologies have made particular skills obsolete in the past, notes Tapani Rinta-Kahila, an information-systems researcher at the University of Queensland in Brisbane, Australia. For example, GPS navigation systems have eroded people’s navigation skills. Generative AI tools, however, are 'he first technology that automates various cognitive faculties around thinking and interpretation, which were long considered unique human skills submitted by /u/tiguidoio [link] [comments]

How I learn with AI without affecting my cognitive ability

I've always worried about using AI for learning or note taking because the process of note taking, like figuring out what is important, the structure etc is part of how we learn and solidify things into memory, but I've found a way to use it without taking away that ability. First, I get the textbook and I read a section. Then I re-read it and figure out what the key points are, and what headings would be relevant for my notes to break down large paragraphs etc. I write these at the side of the book adding dots next to the areas of text I'm referring to (like I'm studying about cognitive behavioural therapy, so if a section is talking about cognitions, I'll write 'cognitions' on the page then things like 'definition', 'background', 'relation to CBT' etc). Then I type these onto a document (I use obsidian) and then go back through the text and add the bits to each heading. Finally, I add my own notes into AI and ask it to create study notes for me. These are the finalised ones that may have more structure or visualisations and make connections between things. I go one step further and then write these down onto paper, as well as copying it onto another obsidian document along with tags and links to other relevant notes for easy access if I don't want to trawl through my notes to find some info. It's not perfect and it's slow but it's helping me remember things better whereas before uploading text into AI and asking it to create notes was doing nothing for my memory (or cognitive ability, ha!) Just thought I'd share. Does anybody else have specific ways of learning through AI that helps them? submitted by /u/psycheyee [link] [comments]