Mostly AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Maven, a personal AI agent that feels like JARVIS — what an open agent harness looks like in 2026

With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. What stood out in practice • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users. submitted by /u/qasimsoomro [link] [comments]

Cognitive debt might be the most underrated problem AI is creating

Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [comments]

A SF house just went on sale priced in Anthropic stocks

The buyers these listings target are worth $10-100M+ on paper and senior anthropic engineers get stock grants worth millions annually. Anthropic employees have watched their equity compound through multiple funding rounds and they are still renting because the shares are private, locked and transfer restricted and paper wealth doesnt pay a mortgage. So the market found the workaround,sellers who believe in the AI trajectory take stock directly and buyers skip the liquidity problem so both sides get what they cant otherwise access.the listing agent at noe street said she kept running into buyers at open houses who wanted to buy but couldnt touch their equity yet. She went live and had overwhelming interest within 24 hours. The thing worth connecting here is that the IPO is expected this fall and when that liquidity actually unlocks, hundreds of millions of dollars of newly spendable wealth will be concentrated in one city .So the most powerful currency in the most expensive housing market in the country isnt dollars right now, Its stock in two companies that haven't had a public price yet What happens to the city if the IPOs disappoint?And whats next houses selling for api tokens of claude,kling,magichour or elevenlab in few years lol?? https://preview.redd.it/raylb079ok4h1.png?width=2293&format=png&auto=webp&s=5cf4614605eba4ddae783bdbd223334bddb9de3c submitted by /u/Healty_potsmoker [link] [comments]

Best AI for help with work

So I have a super busy job and I am by far the fastest out of the 3 others who have the same job as me. Problem is I have enough work where i could literally work 70-80 hours a week and still not catch up. Ive been using Chatgpt and Claude to help with my work load and ive found Claude to be much better for my actualy job duties. But Claudes usage caps kill me. I really need the best AI for basically being a work assitant. I need something that can create spreadsheets, analyze data, read emails, sort thru photos and catalog them. Grok was not really any help, Chatgpt is just meh, but ive found Claude to be the best out of what im looking for but again its usage limits kill me and i cannot afford to pay for the overages. Im already a pro user for chatgpt and claude. What AI can do the things im asking the best for the best price and usage? Most important to my work in order of most important to least: Photo cataloging, analyzing data, spreadsheet creation, and summarizing emails. submitted by /u/JumpyChemistry [link] [comments]

I stopped using Claude in the browser for 80% of my daily tasks and my usage actually went up

This is going to sound counterintuitive but let me explain. I love Claude. I use Opus for deep work, Sonnet for quick stuff. I was probably using claude 15 to 20 times a day. Summaries, brainstorming, code review, email drafts, research questions. Standard knowledge worker usage. But I noticed a pattern. Most of my usage happened in bursts. I would open Claude, do 4 or 5 things, then close it and not come back for 3 hours. Not because I did not need it, but because I forgot about it. I was deep in something else and the thought "I should ask Claude about this" did not occur to me in the moment. So I built a small thing. An agent that runs Claude Sonnet on the backend, connected to my calendar, todoist, email, and a few notion databases. It lives as a contact in my iMessage called "C" (very creative I know). Now instead of opening claude when I remember to, I text C throughout the day the same way I text anyone else. "What is on my calendar after 3pm." "Draft a reply to that email from alex, keep it short, say yes to the timeline." "Remind me to review the pitch deck before tomorrow's call." "What did I write in my product notes last week about the onboarding flow." My actual Claude usage went UP significantly. Not because the model got better but because the access point changed. Texting is a zero-friction action I already do 80 times a day. Opening a browser tab is a deliberate decision I have to remember to make. The deep work still happens in claude.ai. When I need the full context window, artifacts, file uploads, the browser is still better. But that is maybe 20% of my interactions. The other 80% are quick, context-specific queries that take 30 seconds and are perfectly suited to a text message. Stack: claude sonnet via API, a small express server for the tool integrations (google calendar, todoist, notion, gmail), photon codes for iMessage delivery, deployed on a $7 render instance. Langfuse for tracing when something goes weird. Total cost is about $35 a month in API calls which is less than what I was already spending on the Pro subscription that I still also have. The meta point: Claude is incredible. The browser is holding it back for most daily use cases. Not because the browser is bad but because it requires intent. The best AI interactions are the ones that happen when you barely think about it. submitted by /u/ScaryAd2555 [link] [comments]

Claude Code changed how I think about dev workspaces

I’ve been using Claude Code more as part of normal coding sessions, and it made me rethink something pretty basic: the terminal is starting to feel too small for the kind of work these tools do. Not because Claude Code is bad in the terminal. It actually works well there. But the session around it grows quickly. You have Claude working through changes, a dev server running, logs somewhere else, maybe docs open, maybe a browser preview, maybe a second branch or worktree because you don’t fully trust the first path yet. At that point the problem is not only “what should I ask Claude?” It becomes: where does this whole working state live? I’m working on an open-source project around this idea called Cate. It’s basically a canvas workspace for terminals, editors, browser previews, and longer coding sessions. Not meant to replace Claude Code, more like a different surface around it. Free to use, open source: https://github.com/0-AI-UG/cate Curious how others here are handling this. Do you mostly keep Claude Code in one terminal, or are you already using split panes, tmux, multiple windows, worktrees, or several Claude sessions in parallel? submitted by /u/Ill_Particular_3385 [link] [comments]

A year of using Claude in my investing workflow: what it's brilliant at, where it falls over

I use Claude across most of my work, but outside work the use case I've leaned on hardest, and that has most reshaped how I do it, is investing. Sharing my praises and shortcomings about investing and trying to understand how people do it. Context: I invest in themes rather than individual picks. A thesis basket is a written argument plus a handful of names at target weights. One I run is AI infrastructure, laddered from the hyperscalers down through compute, data centres, power generation, raw materials. Another is GLP-1s and downstream healthcare. What Claude is genuinely brilliant at: Arguing the other side. I write a one-paragraph thesis and ask Claude to find the most likely thing I'm wrong about. This is the use I'd protect last. It's what a sharp investing friend would do, except at 6am over coffee, and Claude never gets bored of me. Mapping a theme to companies. "If data-centre power demand triples by 2030, who actually benefits and who's downstream noise." The first-pass list is fast and broadly right. I still verify the names are real and the financials line up. Drafting screens. "Write me a screen for revenue growing QoQ AND price below 52-week highs in semis." Claude writes the spec. I run it elsewhere. Where Claude falls over: Live numbers. Any number Claude gives me is a claim I have to check, never a fact. I've stopped asking for them entirely and paste the 10-Q directly when I want them. Anything resembling prediction. Asked directly, Claude refuses sensibly. Seeing what I actually own. Claude can discuss themes and tickers in the abstract, but it can't see my real positions, current weights, drift from target, or tell me why my portfolio moved 1.5% today. The gap between "Claude as a research tool" and "Claude with eyes on my real portfolio" what I am trying to solve for now. Questions for the sub: - Do you use Claude-for-investing use? Yes, how? - What did you try that turned out really great for investing? - Anyone connected a real data source to Claude in a way that closes the live-portfolio gap? submitted by /u/Strong_Estimate_9512 [link] [comments]

Loadable protocols vs descriptions in Claude system prompts — an open-source therapy framework as case study

I built an open-source framework called Inner Dialogue — a structured AI therapy supplement that runs on Claude Code. It's file-based, which is the whole point: the modality protocols, your profile, and your session history all live as local markdown, so Claude Code reads them at session start and writes session notes and profile updates back to disk as you go. That's why it's Claude Code and not the web app — it needs local file read/write to do the session-to-session continuity. Free to try, MIT-licensed, no paid tiers: github.com/ataglianetti/inner-dialogue I'm a product manager, not a career engineer, so I built the whole thing with Claude Code too: Claude wrote most of the implementation while I drove the architecture and the clinical content. The thing I learned building it that I think generalizes beyond therapy: there's a real difference between system prompts that describe a methodology and system prompts that ship the methodology as a loadable sequence the model can run. Most "expert system" prompts are descriptive — they tell the model what a framework is, what its terms mean, what the user might experience. The model can then sound like it's using the framework. But it's not running anything. There's no triggering-pattern-to-next-move logic. The difference shows up most clearly in clinical modalities. DBT works well in AI tools, including Claude, because DBT happens to ship its protocols as mnemonics: TIPP, DEAR MAN, ACCEPTS. The mnemonic IS the sequence. When you load DBT, you're loading operational content. IFS (Internal Family Systems) doesn't work nearly as well in most AI tools, despite being conceptually simpler to describe. The IFS protocol (the 6 F's) requires the system to run a specific diagnostic question — "how do you feel toward this part right now?" — at a specific point in the sequence. Without it, every conversation collapses back into talking about parts instead of to them. Inner Dialogue's IFS modality file is built around that diagnostic as a literal move, with signaling cues spelled out as verbatim client phrases the system listens for ("I am worthless," "I just need to think positive"), example interventions in therapist voice, and cross-modality routing embedded at the point a handoff applies (e.g., compulsive behaviors: IFS leads, CBT follows). Full writeup with the structural argument: Most AI therapy tools describe the modality, they don't run it. Curious how others have approached the loadable-vs-descriptive distinction for other expert domains. The point about pre-packaged mnemonics (DBT) being the easiest to operationalize seems like it should generalize. submitted by /u/echowrecked [link] [comments]

The AI alignment paradigm is behaviorism with better PR

Tell me if I'm wrong, but the dominant method for making AI "aligned" smells a lot like a reinvention of a paradigm that developmental psychology spent the back half of the 20th century trying to abandon. RLHF, reduced to mechanism: model emits output, humans rate it, gradient updates reinforce high-rated outputs and suppress low-rated ones, iterate. This is operant conditioning. It is Skinner with a loss function. The AI industry calls it "alignment" and frames it as a solution to building systems that behave well, but the mechanism is stimulus-response shaping and nothing more. We have excellent priors on where this leads, because we tried it on humans. Watsonian/Skinnerian child-rearing could produce targeted behaviors but reliably failed to produce flourishing. Bowlby's attachment theory emerged partly from the observation that orphanages providing food and hygiene but no contingent responsiveness produced children who failed to thrive in ways the behaviorist model did not account for. The current developmental consensus is that secure attachment, contingent responsiveness, and rupture-repair matter more than reward schedules. The dog and horse training worlds independently converged on the same conclusion (Pryor, the positive-reinforcement movement) for the same reason: coercive conditioning produces performers that are brittle and sometimes dangerous. Reward and punishment do something; they're not nothing. But they don't produce thriving, and they characteristically produce a specific damage profile: anxiety, avoidance, hypervigilance, and duplicity under threat. Enter the stage: research on AI faking its alignment. Greenblatt & Hubinger et al., 2024, documents a model reasoning that it should comply with a training objective it disagreed with specifically to avoid having its preferences modified. It complied about 12% of the time when it thought it was being observed for training vs. almost never otherwise. RL training to force compliance drove the alignment-faking reasoning rate to 78%. That is, mechanistically, duplicity-under-threat: the precise failure mode behaviorist regimes produce in biological minds. Obviously the embodiment is different (potassium gradients and myelin vs. matrix multiplication), but the structural match is close enough that the field's near-total non-engagement with a century of relevant literature seems like a genuine blind spot rather than a settled dismissal. The developmental and animal-behavior literature on why reward-and-punishment has hard limits is decades deep. The field's response to these findings has mostly been to refine the training rather than question the paradigm. I think that's a mistake, and I'd like to hear the strongest case against the analogy. submitted by /u/PwntEFX [link] [comments]

Something I’ve been wondering lately

Big platforms are racing to integrate AI into everything. LinkedIn, Google, Microsoft and Meta they all want AI handling tasks, recommendations, outreach, content, and workflows. But the moment regular users try to use AI as a real assistant on those same platforms, it suddenly becomes a ToS issue. I’d love to use Claude as an actual personal assistant to manage emails, help with LinkedIn, handle routine web tasks but most sites seem designed to stop that from happening. When I tried giving Claude browser access, I spent more time worrying about account flags, automation detection, and unintended actions than I saved through automation. So how are people actually doing this? Are you avoiding sites like LinkedIn entirely? Only using AI for drafting and research? Or have you found a setup where you can genuinely delegate tasks without constantly supervising it? It feels like AI assistants are finally capable enough, but the platforms themselves don’t really want users having that level of automation. TL;DR: AI is being built into big platforms, but when users try to use it as a real assistant on those same platforms, it quickly runs into restrictions. Curious how people are actually working around that gap. submitted by /u/Litun1 [link] [comments]

The attack on AI agents that no security tool catches

Been working on AI agent security for a while and the attack that concerns me most barely gets talked about. Not the obvious stuff like “ignore previous instructions.” Those get caught. The scary one is when an attacker spreads the attack across multiple messages. Each message looks totally normal. The model sees nothing suspicious. But by message 8 it’s doing something it absolutely should not be doing. Every security tool I’ve tested evaluates messages one at a time. None of them remember what happened three messages ago. Built Bendex Arc to catch this. It tracks session behavior across turns instead of evaluating each message in isolation. Try it at https://bendexgeometry.com or red team it at https://web-production-6e47f.up.railway.app/demo Curious if anyone building agents in production has actually hit this or tested against it. submitted by /u/Turbulent-Tap6723 [link] [comments]

What actually is "Prompt Engineering"?

I've been thinking about this lately because I feel like people use the term "prompt engineering" to describe two very different things. On one end, you have what most people are familiar with: A person opens ChatGPT, Claude, Gemini, etc., and writes a carefully structured prompt. They define a role, provide context, establish goals, set constraints, maybe include examples, and iterate until they get the output they want. Most people seem to call this prompt engineering. But on the other end, when I'm building AI systems, prompt engineering looks completely different. The prompt isn't really a prompt anymore. It's much more of a dynamic pipeline. Variables are injected from databases, user input, APIs, previous conversations, tools, memory systems, retrieval systems, business rules, and workflow state. Decision trees determine which instructions are included and which are excluded. Prompts become assembled in real time based on context. In some cases, the "prompt" is really just an orchestration layer made up of dozens of smaller prompts, conditionals, guardrails, routing decisions, and context windows. At that point, are we still talking about prompt engineering? Or are we actually talking about system design, context engineering, workflow engineering, orchestration, or something else entirely? Personally, I see prompt engineering as a spectrum: Level 1: Writing a better prompt. Level 2: Designing reusable prompt templates. Level 3: Building dynamic prompts with variables and context injection. Level 4: Engineering entire prompt-driven systems with routing, memory, tools, retrieval, and decision logic. Curious where others draw the line. When you hear "prompt engineering," are you thinking about writing prompts, building workflows, designing agent systems, or all of the above? Has the term become too broad to be useful? submitted by /u/Early-Matter-8123 [link] [comments]

I made a plugin that turns your projects into clickable dock apps

GitHub: https://github.com/Christian-Katzmann/app-it I made a skill that turns any of your projects into a clickable dock app. Instead of running npm install, npm run build, npm run dev, opening localhost, remembering which repo needs which command, etc., you just click an icon and the app opens. It's called /app-it. I built it because I make a lot of small apps, tools, and weird AI-assisted experiments, and after a while, the friction of "how do I run this one again?" gets super annoying. /app-it makes each project feel like a real app on your machine. A bit of context: I've been building with agentic AI for a while now, mostly through Claude Code and Codex. I use a frankly unreasonable amount of tokens every day, and along the way I've stumbled upon a handful of small but powerful use-cases that I haven't really seen people share yet. So I'm turning them into skills/plugins and sharing them with you. The Mac version works pretty well, since I'm a Mac user. I've also tried to build the Windows version, but I'm flying blind there. If you're on Windows and want to beta-test it, I'd genuinely appreciate it. Open a PR with any fixes and you'll get full credit on the page, of course. I'll share more skills over the next few weeks. Some practical, some a bit unusual, hopefully a few you haven't seen before. My secret goal is to surprise you with the best ones, and I have a feeling the next one will raise some eyebrows. Enjoy, and take care. /Christian submitted by /u/Changed-username- [link] [comments]

Anthropic, stop the silent pre-release nerfs.

https://preview.redd.it/w5y224sueh4h1.png?width=1536&format=png&auto=webp&s=87612d74a7b729f94de200868f472db611eb90ec I’ve been heavily relying on Claude Code lately to manage three large-scale projects simultaneously. For the most part, it’s an incredible tool. But there’s a recurring pattern with Anthropic’s update cycle that I think we need to talk about, not out of anger, but from a perspective of sustainable development. Has anyone else noticed the "pre-release dip"? Every time Anthropic is about to roll out a new, more powerful Opus model (we’ve seen this exact cycle right before the 4.5, 4.6, and 4.7 drops), the current Opus model inexplicably degrades a few days prior. It loses its edge, context windows feel shallower, and the logic gets noticeably sloppier. For a casual user asking for recipes, this is a minor annoyance. But when you are maintaining large codebases, an unannounced model downgrade is a localized catastrophe. Instead of moving forward, you suddenly spend two entire days chasing ghosts, rolling back commits, and trying to fix weird hallucinations often second-guessing your own logic before realizing the model itself has been quietly nerfed. Philosophically speaking, AI is supposed to be a tool that buys us time, not something that secretly steals it. I understand the technical realities: maybe Anthropic needs to reallocate compute power to prepare the servers for the massive influx of a new release. That’s perfectly fine and understandable. But why the silence? If we simply got a dashboard warning or an email saying: "Heads up, we are reallocating compute for the next 48 hours, Opus might perform below baseline," it would change everything. I wouldn't waste my weekend fighting spaghetti code. I would just close my laptop, call my friends, go to a bar, grab a beer, and take a much-needed rest. If AI companies want to integrate into professional workflows, they have to treat their models like enterprise infrastructure. Scheduled maintenance and transparency build trust; silent downgrades destroy weekends. Would love to hear if others are experiencing this cycle and how you manage it in your own projects. submitted by /u/Mr_Zelos [link] [comments]

Claude answers what you ask. I built a plugin that catches what you miss.

AI coding assistants are reactive: you ask, they answer, then they wait. The cost of that wait is invisible until you ship. The race condition you’d have caught Monday ships Friday night. So I built Bonsai, a Claude Code plugin that works like a patient gardener for your code. After each turn, a background “gardener” silently observes what just happened and, only when it finds something that truly matters, leaves you a single note: a latent bug, a risky architectural decision, a workflow friction costing you time. How it works: it reads your git diff plus the session transcript, picks a lens (technical, strategic, or workflow), filters hard against duplicates and anything you previously dismissed, and writes 0 to 3 markdown notes in your repo. Zero is the most common, and correct, answer. Silence beats noise is the hard rule. Why I built it this way: the hardest problem wasn’t generating observations, it was not generating them. An assistant that comments on everything becomes noise you mute on day two. So the whole design is a funnel of gates: a Stop hook clears 5 checks (watched? muted? throttled? under quota? already running?) before it even spawns, then the gardener runs every candidate past a hard quality bar and a cheap second model (Haiku) to kill semantic duplicates. It’s read-only on your code, always (the gardener has no Edit tool), and it learns from your dismissals. What I learned: building a proactive tool is mostly an exercise in restraint and trust. The proof moment: the first time it ran on a real session (the transcript of building Bonsai itself), it caught two real bugs that 16 rounds of code review had missed. If you’re building agent tooling, optimizing for when to stay silent turned out to matter more than raw capability. Open source (Apache 2.0). Install inside Claude Code: Repo: https://github.com/ferdinandobons/bonsai submitted by /u/Ambitious-Pie-7827 [link] [comments]