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What is actual time line for review connectors directory?

We made an MCP integration for our planner Voiset. Users create separate workspaces for agents and wire them deep into their workflows, so it basically turns into having real workers, not just a chat. Our blocker is onboarding. To connect, a user has to add it as a custom connector, paste the MCP server URL and enter secret credentials manually. That is way too non obvious for a normal person. Most never figure out where the address or the secret goes, and we lose them. We see most of the big companies already passed review and sit in the directory, so their users just click connect. We want the same. What did it actually take for you to get listed on top of the submission itself? Any extra step we are missing? This would honestly fix a real onboarding problem for us. submitted by /u/Capable_Ad_2433 [link] [comments]

Remote PC automation with AI

I've made a tool that I install on a remote pc, and it exposes commands I guess like a MCP server to ai chat on my own pc. It can use the GUI, type, click mouse, take screenshots, run commands directly, edit registry etc. But my god for tasks that take me a human to directly do things the AI takes 10x longer. Installing and configuring programs, apply settings, installing drivers. I want it to setup a new computer for me like I want, and I have an exact guide of things to do. It takes FOREVER and abandons and skips requirements the guide says to do if there is any issue. The mouse did not click where it thought it would, they see some random popup and don't know what to do with it. The prompt is flexible, it says figure it out basically, but it requires me to go in and say wait why did you not do that task? I don't think its the tool we've built, I think its the AI interacting with the tool and just not being quick human judgemental about it. I do not want to install claude desktop or something similar, as I dont want to mess with my claude account access on another computer. My tool does not need an account. I'm not advertising my tool, this is for me, and also it sucks apparantly. I'm asking has anyone been here, and found a better solution for AI remotely actually USEing a computer, like a human, and quicker than a human can? Not just setting registry settings, as thats not all there is to using a computer. thx submitted by /u/radialmonster [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]

Has ChatGPT quietly become your default tool for thinking through problems?

A year ago I mostly used ChatGPT to answer questions or rewrite text. Now I've noticed something different. A few nights ago I was on my laptop trying to figure out a project and without even thinking I opened ChatGPT before opening Google. Not because I expected it to have the perfect answer but because it's become the fastest way for me to organize my thoughts, compare ideas and figure out what to do next. It's kind of strange how naturally that habit developed. I'm curious if anyone else has experienced the same shift. Do you still think of ChatGPT as a search tool or has it become more of a thinking partner for you? submitted by /u/Efficient_Bowl_7008 [link] [comments]

Stop asking Claude for "something creative." Ask it to find the lacuna.

TL;DR: If you ask an LLM for "a novel idea" you get beige mush, because the most probable answer is the average answer and novel is the opposite of average. Instead, make it map a field, find the axis everything secretly optimizes for, locate the cell that the structure implies but nothing occupies, and, the important part, name the force keeping that cell empty. I've been calling it lacuna prompting. It consistently gets sharper, less safe output than anything else I've tried. ______________________________________________________________________________________________________ I spent a long session with Claude trying to figure out why its answers feel like they hit a wall whenever I want a genuinely non-obvious take. Best framing we landed on: a model's knowledge isn't a list of facts, it's more like a near-continuous fabric with gaps in it. The word for a gap in an otherwise continuous thing is a lacuna, a missing tile in a mosaic where the surrounding pattern tells you what the tile should have depicted. That reframe is the whole trick. Don't ask the model to invent from nothing. Ask it to find the gaps in a fabric it already has, where the surround constrains what belongs there. Why the default is beige When you ask for "a creative idea," the model optimizes for the highest-probability response, which is by definition the most conventional one. "Creative" and "most probable" point in opposite directions, so you get something that sounds novel but is actually dead center. The safeness isn't the model being timid. It's regression to the mean wearing a costume. Lacuna prompting works because it forces the output to a specific edge of the space, where the boring center answer is visibly wrong and can't be used. The method Here's the actual procedure. Paste this, fill in the topic: Don't give me a novel idea. Run this on [TOPIC] and show your work at each step. 1. MAP THE FIELD. List the main existing approaches as points. Map them densely enough to see the shape. 2. FIND THE HIDDEN AXIS. What do almost all of them secretly optimize for? Name the one direction the whole field is sliding along without noticing. 3. LOCATE THE LACUNA. Find the cell the surrounding geometry implies should exist but is empty — usually the opposite pole of that hidden axis, or the centroid between clusters that none of them occupy. Describe what sits there. 4. NAME THE FORCE KEEPING IT EMPTY. This is the important step. Is the cell forbidden by the field's own incentives? Unrepresentable in its default mental model? Punished by something structural? If you can't name a specific force, you've found a boring gap, not a real lacuna — go back to 3. 5. SORT IT. Is it empty because nobody's discovered it, or empty because everything there fails? Admit you can't fully tell from inside, but give your read. 6. PROPOSE THE FILL at full conviction, and flag your confidence: is the surrounding pattern dense (strong inference) or thin (you're extrapolating)? The main drivers Step 4 (name the force) is the engine. Anyone can say "here's a gap." The value is diagnosing why the field bends away from it, an incentive, an accounting model, a measurement system, a tooling limitation that literally can't represent the missing thing. If the model can't name a force, the gap is usually boring. When a response drifts back toward safe, it's almost always because step 4 got thin. Push on it: "what's the force?" Step 6 (confidence flag) is the part everyone skips and shouldn't. Make it tell you whether the surround is thick fabric or a thin patch. A model will always produce a fill, that's the catch. It can interpolate just as smoothly over a real gap as over a hole that should stay empty (this is basically what a hallucination is: confident interpolation over nothing). It can't certify which it's doing. But it can tell you how dense the surrounding pattern is, and that's the single most useful piece of metadata for deciding which proposals to actually act on. Example Ask the normal way "give me a new marketing channel" and you get a list of stuff that already exists. Run the method and step 2 surfaces the hidden axis: nearly all marketing optimizes the funnel toward more. More attention, more reach, more conversion. Step 3 walks to the opposite pole: a discipline built on repulsion, where the KPI is who you drove away and the product is having survived the filter. Step 4 names why it's empty: the funnel model literally can't represent a technique whose success metric is a narrow top, and commission structures punish anyone who tries. That's a real lacuna with a named force, not "make a TikTok." Whether it's good is a separate question, which is the whole point of the last step. Caution The method finds the gap and proposes the fill. It cannot tell you whether the floor holds. That part (actually testing it in the real world) is yours, and it's not optional. The model hands you coordinates, not verdicts. If you treat the clean framing

Figuring out how to distribute a Notion page with prompts

A few days ago I put together a small Notion page with Claude to help social media managers in their workflow, with a defined structure and specific 85 prompts. Nothing ambitious, it was more of a playground project, but now I'm wondering how to make the "brain" and the logic behind it as usable as possible for the SMMs. Any ideas? Link to the page: https://produttivitaitalia.notion.site/SMM-OS-English-387e36fd6a8b8117aeabf50424f64e1a submitted by /u/its_temp_yes [link] [comments]

Running Sonnet 4.6 on every Instagram DM for a 7-location restaurant. 97% cache hit is the only reason it's affordable

I figured the agent would be the tough part. Turned out the cost was the real story, and that's what closed the deal. A sushi chain with 7 locations runs about 90% of its orders through Instagram DMs. I put a Claude agent (Sonnet 4.6) on those DMs through the Meta API. It has the full menu, ingredients, calories, allergens, delivery zones, hours, prep times and current promos for all 7 spots. That is a big block of context, and it has to reach the model on every single message, because every reply needs the whole menu sitting in front of it. Normally that kills you on cost. You pay full input price to reprocess that entire block every time someone types "hi." On paper, Sonnet on every DM looks like a non-starter for a chain doing real volume. Caching is what flips it. On roughly 97% of messages, that static block gets read from cache instead of reprocessed, and a cache read runs at a tenth of normal input price. So most of what the agent handles comes in at 90% off. The only full-price tokens left are the customer's actual message and the reply, both tiny next to the menu dataset. That is the whole gap between "too expensive to run per message" and "the owner forgot there's an LLM in the loop at all." What the agent does with all that context: helps people pick, explains what is in a roll, flags allergens, upsells when it fits ("that set goes well with X sauce, want it?"), then pushes the confirmed order to the kitchen and writes a record into the CRM and an admin panel. What I kept off it on purpose: calls, voice notes and photos go to a human. A model guessing at a photo is how you ship a disaster. Plain text handoffs to a person almost never fire, basically just "get me the manager," and even that is rare. I split the prompt so the menu and rules sit in one stable prefix and only the live conversation changes, which is what keeps the hit rate up. Anyone pushed past ~97% on a setup like this? submitted by /u/timhartmann7 [link] [comments]

Is it always this funny?

Been using Codex for bulk stuff before passing off to Claude and they randomly started being funny as hell lol. “I’m upgrading the validator too so that particular gremlin doesn’t get a second career” 🤣 submitted by /u/12thYearSenior [link] [comments]

Anyone here using more than one AI tool in their workflow? How do you handle the context gap?

I've been running Claude for planning and a separate session for building, and the part that keeps breaking down is the handoff. whatever I figured out in one session doesn't automatically carry to the next. Curious how others are handling this. Are you using a single tool end-to-end, or mixing Claude with Cursor/Codex/ChatGPT? And if you're mixing, what's your actual handoff process? submitted by /u/riley_kim [link] [comments]

Claude Code in the terminal vs. the Claude Desktop app — which do you use and why?

Trying to figure out which setup actually fits my workflow better and would love to hear from people who've used both. For those of you running Claude Code (or Claude in general), do you prefer working in the terminal or the desktop app? And more importantly — why? A few things I'm curious about: What kind of work do you mostly do with it (coding, writing, research, automation, etc.)? If you switched from one to the other, what made you switch? Any features in one that you really miss in the other? How does each handle larger projects or multi-file context for you? MCP servers / integrations — is one noticeably easier to set up? I'm a full-stack dev and I keep going back and forth, so I'd really appreciate hearing how people actually use these day to day rather than just the marketing pitch. Thanks in advance! submitted by /u/ghedtoboss [link] [comments]

How do you keep decisions from drifting across Claude Projects sessions?

I've been using Claude Projects for multi-session work on a substantive project using claude.ai chat, and I've landed on what I think is a structural problem that Projects doesn't fully solve: decisions drift. Not context — context is mostly fine with Projects. But decisions. Specifically: A choice I made two sessions ago ("we're not supporting X in v1") gets quietly resurfaced when a tangentially related topic comes up Settled tradeoffs get reopened because Claude doesn't have a reason to treat them as closed You end up re-explaining the same reasoning across sessions, which defeats half the point of a persistent project The fix isn't longer context or better prompting in isolation. The problem feels like there's no mechanism to tell Claude "this is decided — don't drift from it." Project instructions help but they're not designed for this — they're static setup, not a living decision record. Memory feels like a constant running joke to me. "I'll remember that for the future". Sure you will Claude. It feels like Lucy and the football... What I've ended up building is basically a lightweight system on top of Claude Projects: a document that tracks decisions as explicitly closed, with the reasoning attached, and a session open/close discipline that reconciles what's in the document against what Claude actually did last session. It's working. But it took a while to figure out, and I'm curious whether others have hit the same wall and what you're doing about it. What's your current approach for keeping a Claude project coherent across sessions? Specifically on decisions, not just context — are you maintaining explicit decision logs? Prompt scaffolding? Something else? submitted by /u/Solace914 [link] [comments]

Claude Code randomly stopped working overnight… anyone else?

Hey guys I’ve been using Shopify CLI with VS Code and the official Claude Code extension to edit my Shopify theme for the past couple of months without any issues. A few days ago, I opened VS Code and everything seemed to be disconnected. Since then, I haven’t been been able to reconnect Shopify CLI or edit my Shopify store. Claude also no longer works inside VS Code. Whenever I send it a prompt, it just sits there showing the thinking/loading animation indefinitely and never actually responds. To make things worse, the VS Code UI seems different now. I don’t even see the Shopify project/folder I used to open, so I’m completely lost. I’ve spent days trying to figure it out, but I don’t really know my way around VS Code. Has anyone else had this happen recently? This literally happened over night without changing anything. Did something change with Shopify CLI, VS Code, or Claude? Any guidance would be hugely appreciated! submitted by /u/Heavy-Photograph-250 [link] [comments]

is AI making content creation too easy and distribution the new bottleneck

been thinking about this a lot lately. with all the AI tools available now, generating content has become almost trivially easy. blog posts, social captions, video scripts, email sequences, you can spin all of that up in minutes now. but here's what i keep noticing. the creation side got solved and now distribution is the part nobody has really figured out yet. you can produce 10x more content than before but getting it in front of the right people consistently is still just as hard if not harder. feels like AI optimized one half of the equation and left the other half exactly where it was. or am i missing something and people have actually cracked distribution too has AI genuinely changed distribution in a meaningful way or is it still mostly a manual grind once the content is actually made submitted by /u/IntegritypneicAR [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]

Can anyone tell me how this was made, I'm breaking my head but can't figure it out

This ai influencer has gotten me going crazy , how is this profile posting multiple reels a day all of same MID quality. I've thought of all the model they could be using but I can't recreate the same effect using ,comfyui REActor,inswapper, but it's quality doesn't match that of kling or higgsfield and it doesn't look completely animated either. So someone please help me deconstruct this workflow. The ai model itself is good but the overall video quality is sometimes good and sometimes bad. submitted by /u/DrALUCARD2 [link] [comments]