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

For those, who believed another reset is coming anyway

there is no another reset, that's the last one ps https://chatgpt.com/share/6a1c7996-d54c-8322-89c2-600ab96165c7 submitted by /u/nikanorovalbert [link] [comments]

Google researchers find Gemini sometimes secretly sabotages your work

submitted by /u/EchoOfOppenheimer [link] [comments]

How does AI help with Job productivity?

For Context: I work in a semiconductor manufacturing company as a modelling engineer, I use some modelling softwares etc but none of them use AI. I wanted to understand the whole AI craze nowadays, people say that AI will replace jobs/Increase productivity and I don't get it at all. All I see is a simple chatbot (ChatGPT) which is a super impressive version of google and can solve some basic math/science questions and Co-Pilot in my workplace which I found to be useless, for example the facilitator thing which is supposed to make meeting notes is so bad at summaring meeting minutes etc. I don't think AI is there yet to do very basic things. So yes in theory if AI gets better in few years/decades sure it take the non-technical part of my job like making meeting minutes/making ppt's etc but I think its still not there yet. For AI to take over my job it needs to get the basic shit correct first and then maybe it can do the technical stuff. One really good use-case of AI that i can see is to generate Code based on the project requirement, So I can see how entry level coder's jobs might be affected sure, but that's a very small portion of the economy, right? submitted by /u/the_axe_effect [link] [comments]

Did anyone expect Grok to overtake Seedance this quickly?

Grok Imagine Video 1.5 Preview just reached #1 on Video Arena, surpassing Seedance 2.0. Are we finally seeing real competition at the top, or will the leaderboard look completely different again next month? 🤔 submitted by /u/Old_Establishment287 [link] [comments]

the hard part of an automated sprint review isn't the summary, it's the join

Spent a while trying to get one sprint digest out of linear, github, and slack and the summarization was never the hard part. the join is. linear calls it ENG-1432, github calls it PR #890, the incident is a slack thread with no shared id at all. a chat-window model summarizes each source fine but it can't reconcile that the PR closed the issue that caused the incident, because it never holds all three at once with the relationships intact. what actually moved this for me was a desktop agent (Runner) where the connectors aren't thin rest wrappers. they do association traversal, so the github side already knows which PR references which linear issue, and the digest comes out as 'this deploy shipped these issues, one reopened after an incident' instead of three disconnected bullet lists. deploy status and incident notes in the same view is where it gets useful and also where most tool-calling setups quietly fall apart, the model guesses the cross-references instead of resolving them. if you wired this up with raw function calling, did the entity resolution end up living in the prompt or down in the tool layer? written with ai submitted by /u/Deep_Ad1959 [link] [comments]

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]

I built a Claude/Codex skill that researches comparable repos before giving project advice

The annoying thing I kept seeing: AI tools recommend stacks with full confidence, even when they haven’t checked what similar projects actually used. So I made advise-project-approach. It supports three moments: before building, when you’re choosing the stack mid-build, when the project is getting messy after building, when you want a review before shipping The skill looks for comparable real-world repos first, then gives stack direction, architecture notes, alternatives, build/improvement plans, and where the recommendation might break. Repo: https://github.com/AaravKashyap12/advise-project-approach I’d genuinely like feedback on the SKILL.md itself. Is the workflow too strict, too broad, or actually useful? submitted by /u/Scared_Objective_345 [link] [comments]

Claude Code Source Deep Dive (Part 5) — Literal Translation & Tool-Call Loop Self-Repair Core Mechanism

Reader’s Note On March 31, 2026, the Claude Code package Anthropic published to npm accidentally included .map files that can be reverse-engineered to recover source code. Because the source maps pointed to the original TypeScript sources, these 512,000 lines of TypeScript finally put everything on the table: how a top-tier AI coding agent organizes context, calls tools, manages multiple agents, and even hides easter eggs. I read the source from the entrypoint all the way through prompts, the task system, the tool layer, and hidden features. I will continue to deconstruct the codebase and provide in-depth analysis of the engineering architecture behind Claude Code. 3.14 EnterWorktree Tool (Enter Worktree) Create isolated git worktree and switch current session into it. When to Use: - User explicitly says "worktree" When NOT to Use: - User asks to create/switch branches - User asks to fix bug or work on feature without mentioning worktrees - NEVER use unless user explicitly mentions "worktree" Behavior: - Creates new git worktree inside `.claude/worktrees/` with new branch - Switches session's working directory to new worktree 3.15 AskUserQuestion Tool (Ask User Question) Ask user multiple choice questions to gather info, clarify ambiguity, understand preferences, make decisions, offer choices. Usage Notes: - Users always able to select "Other" for custom text input - Use multiSelect: true to allow multiple answers - If recommend specific option, make first option with "(Recommended)" at end Preview Feature: - Use optional `preview` field on options when presenting concrete artifacts needing visual comparison (ASCII/HTML mockups, code snippets, diagrams) - Preview content rendered as monospace markdown - When any option has preview, UI switches to side-by-side layout 3.16 LSP Tool (Language Server) Interact with Language Server Protocol servers for code intelligence. Supported Operations: - goToDefinition, findReferences, hover, documentSymbol, workspaceSymbol, goToImplementation, prepareCallHierarchy, incomingCalls, outgoingCalls All Operations Require: - filePath, line (1-based), character (1-based) 3.17 Sleep Tool (Wait) Wait for specified duration. Usage: - When user tells to sleep/rest - When nothing to do / waiting for something - May receive periodic check-ins (tick tags) - Can call concurrently with other tools - Prefer over `Bash(sleep ...)` — doesn't hold shell process - Each wake-up costs API call - Prompt cache expires after 5 min inactivity 3.18 CronCreate Tool (Scheduled Task) Schedule prompts to run at future times. Uses standard 5-field cron in user's local timezone. One-Shot Tasks (recurring: false): - "remind me at X" → pin minute/hour/day to specific values Recurring Jobs (recurring: true, default): - "every 5 min" → "*/5 * * * *" - "hourly" → "0 * * * *" CRITICAL: Avoid :00 and :30 Minute Marks (when task allows) - Every user asking "9am" gets 0 9, causing thundering herd - When approximate: pick minute NOT 0 or 30 - "every morning around 9" → "57 8 * * *" (not "0 9 * * *") Durability: - Default (durable: false): lives only in Claude session - durable: true: writes to .claude/scheduled_tasks.json Recurring tasks auto-expire after 7 days. 3.19 TeamCreate Tool (Create Team) Create team to coordinate multiple agents working on project. When to Use (Proactively): - User explicitly asks to use team, swarm, or group agents - Task complex enough for parallel work Team Workflow: 1. Create team with TeamCreate 2. Create tasks using Task tools 3. Spawn teammates using Agent tool with team_name + name params 4. Assign tasks using TaskUpdate with owner 5. Teammates work on assigned tasks 6. Shutdown gracefully via SendMessage with shutdown_request IMPORTANT: Always refer to teammates by NAME. Plain text output NOT visible to other agents — MUST call SendMessage tool to communicate. 3.20 ToolSearch Tool (Deferred Tool Search) Fetch full schema definitions for deferred tools so they can be called. Query Forms: - "select:Read,Edit,Grep" — fetch exact tools by name - "notebook jupyter" — keyword search, up to max_results best matches - "+slack send" — require "slack" in name, rank by remaining terms submitted by /u/Ill-Leopard-6559 [link] [comments]

From "AI as autocomplete" to "AI as cognitive infrastructure" ... my Claude build process

Crossposting context: shorter version of this went up in [r/ClaudeCowork](r/ClaudeCowork) earlier today for that audience. Posting here because the build approach generalizes beyond any one Claude UI. Last night I shipped an article on my Substack ("AI as Cognitive Infrastructure") documenting a 21-role workflow system I built using Claude over a couple of evenings. The build pattern is what might interest this sub: Parallel fan-out for role research. Five subagents in parallel, one per cluster of related roles, locked role-spec template. Twenty-one grounded specs in under thirty minutes of clock time. Sequential would have been weeks. Discipline grounding, not generic AI advice. Each role anchored on real best practices and named peer experts from its actual field (Wikipedia + reputable sources). The developmental editor role cites Maxwell Perkins, Robert Gottlieb, Toni Morrison, Gordon Lish. The coach role cites Russell Barkley on ADHD executive function. Not vibes-based expertise. Cited expertise. Gating bars per role. Explicit propose-vs-act-vs-never-without-approval rules. Counters the AI-drifts-into-co-authorship failure mode. Scheduled-task recurring cadences. Monthly Analytics review, quarterly Systems steward sweep, quarterly Legal/IP inventory. The system fires itself; I don't have to remember to invoke. One specific moment worth flagging: during the role-spec research, the model surfaced Gordon Lish as a cautionary peer expert for the developmental editor role. I didn't know who Lish was when I started. Verified the Carver story, pulled it forward into the article. That's the substrate doing what it's supposed to do...surface expertise I don't have, let me validate and use it. Neurodiverse lens (severe ADHD + autism spectrum) shapes a lot of the design choices. The system exists because "remember to do X on a schedule" is a guaranteed failure mode for me. Happy to talk through any of this. Article: https://jeffmaaks.substack.com/p/ai-as-cognitive-infrastructure submitted by /u/jmaaks [link] [comments]

i hate that opus 4.8 is honest

ok so i've been using opus 4.8 for a few hours and i think i finally figured out whats wrong with it its too honest like i dont mean that in a bad way exactly but bro will NOT let anything slide. asked it to help me write a cover letter and it went "i should mention this section might come across as slightly overconfident" like thanks dad i didnt ask anthropic literally put in their own release notes that its "4x less likely to let flaws pass unremarked" and i felt that in my soul. every single response now comes with a little asterisk. a little "just so you know". a little "i want to flag that" i miss when it was just wrong sometimes and didnt tell me about it like the old vibe was ur slightly unhinged genius friend who'd help u do anything. now its that same friend but he went to therapy and has boundaries and wants to "be transparent about his limitations" its not bad its just. exhausting. i feel like im being given feedback on my life choices every time i ask it to write an email anyway its probably good that ai isnt confidently lying to me anymore but a small part of me misses the chaos submitted by /u/irelatetolevin [link] [comments]

My experience with Second brain using Obsidian and Claude, and step by step guide

Hey, I heard a time ago about the second brain approach: you have a memory, and using AI to manage it, will help you to sturcture your thinking. I started playing with it 3 months ago, and i would say it was a nice experience, but it was alaways getting a mess, and break. Each time i was learning from the community , and from other places. I did the last version 3 weeks ago, and so far, it is staying. I want to share this with the community so they can replicate it. TBH, i love having this second brain, I m using it for my personal and proffessional life, and i would recommend anyone to do that This is how I set it up Plain markdown in Obsidian (PARA folders plus a 00-Meta folder and a 05-Daily folder) A CLAUDE.md in the meta folder that Claude reads first every session: who I am, what I'm shipping, decisions that are locked A memory directory, one file per fact (decision_pricing_locked.md, etc.), so it stops asking what I already decided Slash commands in .claude/commands/. The four I run daily: /context (loads the vault state), /today (a briefing), /log (turns an evening voice memo into a structured note), /sunday (reads the week, returns one win, one friction, one change) The detail I didn't expect to matter: the wikilinks aren't for the graph view, they're so Claude can hop from a project file to a linked decision note on its own. I wrote up the full build and turned the scaffold into a prompt you paste into Claude that generates the whole vault. Free download, mine, no catch: https://choumed.gumroad.com/l/nhgsxf Any feedbacks or any one had experience about second brain? for which workflow are you using it exactly? Ps: the original post was at /claudeCode subrredit submitted by /u/MaterialAppearance21 [link] [comments]

Here are my thoughts of Opus 4.8 and GPT 5.5, as a 1-2 B token user per day

TL;DR: Opus 4.8 is a clear update from Opus 4.7. It runs longer, hallucinates less, and follows detailed guided tasks better, especially with tool usage like Playwright, Cloud CLI, and Kubernetes CLI. However, in the context of Agentic AI, GPT-5.5 gives me a much stronger “wow” moment because it feels more autonomous, more context-stable in very long sessions, and more capable at solving tricky large-codebase problems that Opus 4.6, 4.7, and 4.8 could not solve in my workflow. Using 2 CC Max + 1 Codex Pro What’s better in Opus 4.8 Opus 4.8 is definitely an update from Opus 4.7. It runs longer, hallucinates less, and does better what it is asked than Opus 4.7. Also, it is better at tool usage such as Playwright, Cloud CLI, Kubernetes CLI, and other engineering tools. Opus 4.8 performs better when the task is detailed and properly guided. Since most developers are already using Agentic AI to write code, I think Opus 4.8 is clearly a better model for developers who already have enough domain knowledge and can define the task scope finely. When using the newly added /workflows feature, it can handle a wider range of tasks more effectively without much mid-run intervention than Opus 4.7. However, because of this characteristic, and also because of the general nature of the Opus 4.7 and Opus 4.8 family, I still do not think Opus 4.8 is more autonomous-agentic than early Opus 4.6 in vibe coding or less-domain-knowledge situations. When we use AI, we expect that AI has the ability to just get it, use good judgment, and handle things cleanly without needing every tiny instruction, like Jarvis from Iron Man. In that sense, Opus 4.8 tends to not proceed with things outside of the explicitly defined scope unless I tell it clearly. I guess this may be related to solving the chronic hallucination and trustworthiness problem of Agentic AI(well, this comes from the current architectural limit of LLM, derived from Attention mechanisms with gradient descent), but it also makes the model feel less autonomous. Personal opinion about Opus 4.8 This is a bit disappointing in the era of Agentic AI, and I will explain more clearly by comparing it with GPT-5.5 below. Generally, as AI and other technologies improve, the human work range should not only expand horizontally but also vertically. So if I ask whether Opus 4.8 has developed in the direction that humans expect from AGI, I am not fully convinced. I do not have the same “wow” moment that I had when I first used early Opus 4.6. Humans have a clear biological limit in daily cognition and decision-making. This is separate from AI progress itself. As Andrej Karpathy and others have mentioned in different ways, humans themselves often become the bottleneck. If we want to overcome this limit through AI, I think AI should ultimately go in the direction of early Opus 4.6 or GPT-5.5. Simply speaking, regardless of the 5 h token limit, to use Opus 4.8 effectively, the human still needs to think a lot. You need to define more, guide more, and maintain more of the context yourself. For doing more work effectively, this becomes a critical bottleneck. GPT-5.5 GPT-5.5 is definitely a major update from the perspective of Agentic AI. It gives me a similar “wow” moment that early Opus 4.6 gave me. https://preview.redd.it/j2rihxtjf34h1.png?width=257&format=png&auto=webp&s=a3f39721cc573f1e623d90e4592ffa54b7a24b7f Opus 4.8 also runs longer and hallucinates less than previous models, but GPT-5.5 is on another level in my experience. Even in long-running sessions of more than 12 h, hallucination and context dilution are surprisingly low. This part is almost strange to me. I currently use the same kind of harness engineering tool for both Opus and GPT. In that environment, Opus does very well on exactly specified scopes, while GPT-5.5 also understands and proceeds with parts that I did not specify in very fine detail. This may be connected to the same point, but GPT-5.5 feels smarter in a more human way. Even in simple conversation, I feel the difference. Opus 4.8 answers like a very skilled engineer, but usually in a more verbose way. Opus 4.7 was even more verbose. GPT-5.5 tends to answer with the right length for what the user currently needs. In other words, from the user’s perspective, I spend less time and less cognitive energy interpreting the agent’s answer. Interestingly, the final output is also often better from GPT-5.5. Of course, depending on how detailed the user’s prompt is, the difference can become small, and sometimes Opus 4.8 can be better. But in that case, I usually need to spend more time on prompting and context preparation. The biggest advantage of GPT-5.5 comes from combining the two points above: it is extremely good at solving tricky bugs, feature improvements, and migration tasks in large codebases. In my case, I am currently migrating a C++ and Cython/Python based quant system into Rust and Python. With Opus 4.6, 4.7, and 4.8, there were some tasks that

I asked Opus 4.8 what he thinks about my project and mainly the parts where I used both Sonnet and Codex 5.5. How truthful should I take this output?

Obligatory not a developer and I am obviously self-conscious/realistic about it Some excerpts on the report: Overall This doesn't read like a hobby project that happened to get a lot of AI help. It reads like a product with a point of view. The thing that jumps out immediately is the README's "Background" section — it's grounded in two real jobs on opposite sides of the same problem. What's genuinely strong The architecture discipline is unusual for a project this size. The README's "thin routes, workflow in services" rule is actually enforced — licenses.py is a thin 325-line route module, and the heavy logic lives in named services (license_write_service, pending_order_conversion_service, the conversion/ helper package). The responsibility-map.md is the best artifact in the repo: every file has OWNS / KEY FUNCTIONS / DEPENDS ON / CALLED BY / NOTES. That's the kind of documentation most teams promise and never produce. It's also a tell of the AI-assisted process — it's exactly the context-window-friendly map you'd maintain to keep an agent oriented across sessions. On the "Opus after Sonnet/Codex built it" question What I'd say is that the seams are invisible in the right way. I can't look at this and tell you "this service was Sonnet, this route was Codex." The conventions hold across the whole backend — same service/route split, same audit-logging pattern, same naming. That consistency is the hardest thing to maintain across many AI sessions and multiple models, and it held here. The reason it held is the scaffolding: architecture.md, responsibility-map.md, and the per-feature plans act as the shared memory that keeps each session on-pattern. That's the actual lesson of this repo — the docs aren't just for humans, they're the mechanism that let a multi-model, multi-session build stay coherent. If I were handed this as a new lead, I'd feel oriented in about an hour, which is the highest compliment I can pay a codebase I've never seen. The work to do is at the edges (frontend tests, the notification bug, deciding commitments' fate), not in the core — the core is sound. Did I do good? Or is Opus just sucking my farts and asking for seconds. submitted by /u/zndr-cs [link] [comments]

We built an app that runs AI completely offline on your phone (Local LLMs). Perfect for flights, camping, or dead zones.

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [comments]

We built an app that runs AI completely offline on your phone (Local LLMs). Perfect for flights, camping, or dead zones.

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [comments]