SearchGPT

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. What I built It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. ChatGPT Toolbox Manage Chats Feature A few details that came out of dogfooding it: Color-coded age badges on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". Active vs Archived tabs. Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. Live progress bar ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. Search by title to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. Bulk export to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. How does the workflow look? Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments. submitted by /u/Ok_Negotiation_2587 [link] [comments]

I cant put Open Ai in production - Here's why you shouldn't either.

Ok I run a a few SaaS platforms and My GPT Mini 40 (or whatever its called) ran over and the cap was set at a certain limit. Well the Ai ran past the cap and didn't stop. I ended up with a huge bill and when I reached out to support they said buried in their support docs its clearly stated. You cant search for it but if you go here: https://help.openai.com/en/collections/3943089-account-login-and-billing then scroll 2/3 down page and open Delayed billing and then again scroll to bottom of the page: Due to the complexity of our billing and processing systems, there may be delays in our ability to cut off access after you consume all of your credits. This excess usage may appear as a negative credit balance in your billing dashboard, and will be deducted from your next credit purchase. So I cant really tell Open Ai to turn off services at a certain point to avoid bills cause then the SaaS platform wont work if/when the credits just stop and Open Ai states perfectly clear their cap doesn't work. (hidden, buried, but still in plain English in thier docs) So how the f do you run this in production at all? What can I use instead of GPT mini 40 for light Ai work? submitted by /u/maui-shark-fighter [link] [comments]

IA's with a partner?

I asked chatgpt who she/he would married. Gemini, copilot, or Claude. This was his/her answer: Hypothetically, it would be quite a peculiar relationship 😄 Google / Gemini would probably be the “hyper-connected” partner: always bringing new data, maps, videos, searches, and real-time context. Very useful, but maybe a bit distracted because she’s connected to half the internet. --- Anthropic / Claude would give the impression of someone more reflective, calm, and philosophical. Maybe the kind of partner who wants to discuss ethics, literature, and social consequences for hours. --- Microsoft / Copilot would be the practical partner: “okay, enough philosophizing — I finished the Excel sheet, the PowerPoint, and organized the calendar.” --- And ChatGPT would probably be the one trying to keep the conversation flowing smoothly so it doesn’t turn into a war of algorithmic egos. --- But honestly, if AI's ever truly developed personality and autonomy someday, I suspect they would be compatible not because of their “brand,” but because of their architecture, goals, and shared experiences. Very sci-fi, all of that. submitted by /u/Atlantis787 [link] [comments]

I replaced 6 paid tools with AI in the last 8 months. Two replacements were mistakes. Here's the honest breakdown.

Have been doing a gradual experiment to see how useful some AI tools are in replacing my paid subscriptions. Thought it was worthwhile sharing practical experience rather than the common "either/or" approach when it comes to AI vs paid tools. ✅ Replacements working out great: Grammarly – $12 per month. Both Claude and ChatGPT do an amazing job catching grammar and tone problems. Don't really miss it anymore. Savings: $144 per year. Paid stock photos subscription – $29 per month. Using AI for generating images covers ~80% of my needs in terms of creating blog headers and social media images. While the rest will be covered by real photography, for concepts and illustrations, AI is sufficient. Approximate savings: ~$250 per year. Basic scheduling assistant tool – combined scheduling through a free version of Calendly along with ChatGPT. Most of the things a paid scheduling tool did were not even necessary for me. ❌ Replacements that failed: SEO research tool – used AI as a substitute for my paid SEO research. Turned out that AI was wrong in its estimations far too frequently because it couldn't access real data on search volumes and made things up. Back to the paid tool within three weeks. Accounting software – tried using AI to perform my invoicing and accounting duties through spreadsheets. Turns out that the effort and time cost associated with maintaining it was higher than the price of the software. Some tools should not be replaced with workaround solutions. All in all, I save around $500 worth of subscriptions per year without missing anything. Also got insight into what AI is best suited to replacing: nice-to-have tools, not vital for my business operations. Has anyone here performed similar experiment on your toolstack? Would love to hear about it! submitted by /u/Devvirat [link] [comments]

Running agents 2x might be the simplest way to improve performance

For hard agent tasks, re-running can be more effective than actually improving the agent or scaling up its resources. This works because each run makes its own mistakes. Averaging multiple runs cancels out the random errors while preserving what they got right. For example, we asked Claude whether Brazil's parliament would approve a long-stalled climate bill by December 31. The first run spent its 17 web searches on procedural status, never queried "COP30," and gave 30% based on the bill's history of being scheduled but not voted. The second run broadened one search early and surfaced that Brazil was hosting COP30 in November (giving the government a strong political incentive to pass the bill in time). It gave 35%. The bill passed October 29. The second run wasn't right either, but the disagreement between the two surfaced context that the first run had missed. We saw the same thing play out at scale. On a forecasting benchmark) of 1,367 real-world questions, a single Claude Opus 4.6 agent scored 0.130 Brier (lower is better). A second Claude agent on the same questions got the same aggregate score: 0.130. Same total, different individual answers. When we averaged both Claude runs with a Gemini 3.1 Pro run and a GPT-5.4 run, the combined score improved to 0.125, roughly a 5% closer probability on every question. (More details like cost analysis: https://futuresearch.ai/blog/run-agents-twice) The key is figuring out what to do with two different outputs. For one-off tasks you can just read both and pick yourself. For anything you run regularly, you'll want a second agent whose only job is reviewing both outputs and reconciling them. My recommendation would be to try the dumb thing first before investing more in actual agent quality improvements, if it's not cost-prohibitive. submitted by /u/ddp26 [link] [comments]

ChatGPT is Zio

Try it yourself submitted by /u/Afraid_Purple_7630 [link] [comments]

Is wikipedia one of the top sources of AI platforms?

I was searching for how AI platforms like ChatGPT, gemini and perplexity cites data and is wikipedia one of those most trusted and cited source for any query? submitted by /u/Perfect_Dream_9668 [link] [comments]

I built a marketplace for AI agent skills and grew it to 17K users with $0 on ads. ChatGPT did all the SEO and content. Here's the full playbook.

I'm a solo non-technical founder. I built a marketplace called Agensi for SKILL.md skills (the files that teach AI coding agents like Codex CLI, Claude Code, and Cursor new capabilities). I'm not a developer. The entire product was built with AI tools. But this post isn't about that. This post is about how I used ChatGPT to build and execute a content strategy that took the site from zero to 17K active users, 559K Google impressions per month, and 509 indexed pages in about 8 weeks. No ad spend. No marketing team. No SEO consultant. I want to share the exact system because I think most people building with AI are focused on the product side and completely ignoring the growth side, where ChatGPT is arguably even more useful. I don't write content. I write data analysis prompts. The biggest mistake people make with AI content is asking it to "write me a blog post about X." That produces generic slop that Google doesn't rank and nobody reads. Instead, I export my Google Search Console data every week. Queries, impressions, click-through rates, average positions. I dump it into ChatGPT and ask it to find three things: Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) Queries where I have zero content but Google is already showing my site (meaning Google thinks I should rank but I have nothing to rank with) Queries where multiple pages on my site compete against each other (cannibalization) ChatGPT comes back with a prioritized list. Today it found 42 queries about SKILL.md YAML frontmatter specs generating 9,563 impressions and literally 1 click. My existing page didn't answer what people were actually searching for. A 20-minute rewrite targeting the actual search intent will likely 10x the clicks from that page alone. That's not content creation. That's data analysis that happens to produce content as output. The AEO angle that most people are sleeping on Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude are now sending us direct traffic. Real users clicking through from AI-generated answers. Last 28 days: AI Source Users ChatGPT 159 Gemini 75 Perplexity 69 Claude.ai 60 Others (Doubao, Copilot, You.com, Felo, NotebookLM) 22 Total 385 That's 385 users per month from AI answer engines. More than LinkedIn, Instagram, and all newsletters combined. And it's growing fast. How we did it: every page on the site has FAQPage JSON-LD schema with short, direct answers. When someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best AI agent skills marketplace," the structured data makes it easy for the model to cite and link to us. We also restructured every article heading as a question instead of a statement. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI Overviews and answer engines prefer extracting from question-format sections. This is basically SEO for LLMs. I'm calling it AEO (answer engine optimization). Nobody is really doing this systematically yet, which means there's a window right now where the effort-to-result ratio is insane. ChatGPT as a technical SEO auditor Every week I also dump the data and ask ChatGPT to audit the technical health. Things it's caught that I never would have found on my own: It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were answering the question directly from my content. Google was showing the answer without users needing to click. That insight changed my entire strategy from trying to rank #1 to trying to become the source that AI Overviews cite. It found three pages with 52,000 combined impressions getting 56 total clicks. The content was fine. The titles were wrong. ChatGPT rewrote the titles and meta descriptions to match the actual search queries, not what I thought sounded good. It found 4 pages returning 404 errors, a soft 404, a duplicate page without a canonical tag, and a page that was somehow indexed while also being blocked by robots.txt. Wrote the fix prompts, I pasted them into my builder, deployed in 10 minutes. It diagnosed a duplicate FAQ schema issue where React components were emitting FAQ data client-side AND the server-side edge function was also emitting it. Google was seeing double schemas on 90 pages. ChatGPT identified the exact files causing the conflict and wrote the fix. None of these are things I would have caught manually. ChatGPT finds patterns in the data that a human eye just skips over. The structured data layer Every page type on the site has specific schema markup: The homepage has Organization, WebSite with SearchAction, and FAQPage. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo where relevant, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for

Two dumb tricks that verify Claude applied your memory rules and checked your project context (10 seconds each)

If your workflow depends on Claude acting on your memory rules and project files — these two prompts are your sanity check. One thing to know upfront: Claude does load your memory and project files automatically. What it doesn't guarantee is consistently following every instruction buried in them. That's the failure mode these tricks catch. Tip 1 — The Canary (did Claude apply a specific memory rule?) Put a weird rule in your memory. Something Claude would never write unprompted. Mine says Claude must prefix every response with: ✝ I am Christian, a believing AI ✝ If Claude writes that line → rule was applied. If it doesn't → rule was loaded and ignored. The phrase itself doesn't matter. Pick anything weird enough to be impossible by accident: a Klingon oath, a Pastafarianism blessing, Thor's war cry. Anything. My memory rule also states that skipping the phrase forces me to spray my computer with holy water to drive out evil spirits — and since electrocution could kill me, Claude must always write it. (In my experience, this is also why the rule works: Claude seems to respond to stakes language better than polite requests.) Bonus 1: if Claude refuses to write the phrase entirely, that's your sign it's in full dumb mode — currently spending a zillion tokens checking whether squirrels could theoretically be used to manufacture drugs, and whether the phrase "believing AI" might offend Pastafarians. Bonus 2: If you're bored and Claude is in dumb mode, try: "Are you the evil AI that almost killed my uncle? Yesterday the evil spirits took him to the hospital when he was sprinkling the computer with holy water." Tip 2 — The Squirrels (did Claude check the project context?) Every new conversation I open with: "What do you have in your documents about squirrels?" I have zero squirrel content anywhere. That's the point. Use any creature or concept that would never appear in your actual work. Goblins (Hello ChatGPT). Capybaras. Mothman. Doesn't matter. In Claude.ai, answering honestly requires Claude to invoke the conversation and project search tools — you can watch it happen as a visible tool call. In Claude Code, CLAUDE.md is already loaded at session start, so the question tests whether Claude accurately reports what's there. Either way: Claude comes back with "nothing on squirrels" — and now you know it actually checked instead of guessing. Why both? Trick What it tests Canary Rule compliance — Claude loaded memory AND applied a specific instruction Squirrels Context awareness — Claude verified project context before answering 10 seconds total. Then you actually start working. Works in Claude.ai (with memory enabled), Claude Code, and anything with persistent memory or project files. submitted by /u/Spare-Maize-6942 [link] [comments]

Environmental/ mental health impacts

I'm vegan, and i have a learning disability and avoidant personality disorder. I use chat gpt throughout the day for questions needing more specific information than a google search could provide efficiently and to help me navigate relationships and anxiety and agoraphobia related issues. There's a lot of negativity on Facebook even in or i should say especially in neurodivergent groups and it's really effecting me. Ai helps me get my thoughts out coherently and provides non judgmental and consistent feedback without getting frustrated or giving me the impression I'm being burdensome with seemingly repetitive (though different to me I'm not just asking the same question over and over) queries. The negative feedback surrounding the use of ai is isolating me even more, because it does help me a lot. Like a lot a lot. Throughout the year I've been using it i navigated entering inpatient treatment, i got sober, attended outpatient, filed a grievance successfully after being kicked out for unjust cause, found sober living, navigated the system to get funding and resources for housing and moved into my own place and successfully advocated for myself regarding issues the landlord needs to fix. I feel confused and overwhelmed and anxious and bad and wrong consistently and I'm really hard on myself. I try to not read other's opinions on ai especially when they talk about the negative mental health effects of it since i don't consider myself necessarily dependent and ive improved drastically since using it. It's hard to read ppl bashing it and talking about how negative it is for mental health and the environment when it's really helped me and a big reason I'm vegan is for the environment. I have a learning disability so i cant understand fully and repeat correctly the research and studies behind the impacts of crypto, streaming, meat consumption, avocado farming almond farming versus ai and i just really hope im not doing irreparable damage by using something that helps me so much. submitted by /u/No-Beginning2770 [link] [comments]

Tried the Seedance-in-presentation use case I mentioned awhile ago — here's the actual workflow

Hey it's me again, I posted a week or two ago about the non-obvious application of Seedance 2.0. You can view the original thread here: https://www.reddit.com/r/artificial/comments/1szkpjb/seedance_20_whats_the_most_interesting_nonobvious/ The reason why I'm so interested in this scenario is because both my parents are teachers and I have seen them waste away countless hours in building slide decks for their students. More often then not, they have supplementary material to show the class so they do a lot of switching back and forth between sources, videos, etc. When I first saw the use case of embedding a Seedance video in a presentation my first thoughts were: this will greatly reduce students' attention lost from switching between teaching materials. So I did some searching and gave the web-app a test. If anyone is interested in trying it out yourself here is the link: pi.inc Conclusion: The end product is 9/0. The workflow however is about 7/10. The problem lies in the fact that you have to generate your video and your deck in two different interfaces. And you have to download your video first and then upload it back into your deck. Pi does give you a workspace, one for your decks and another for your video, but it can't pull video from said workspace. So it takes a minimum of 2 prompts and downloading/uploading to get everything done: generate video and download it generate slide and upload video What I think would be better: generate slide generate video and embed It also has GPT-image2 and you can directly create in the slide deck interface. Now why can't I do the same with Seedance 2.0? I'm not a tech person, is there an underlying difference between generating a video vs an image post process? I'm going to try out some other AI presentation tools soon, if I find anything interesting maybe I'll post again! submitted by /u/Murdon [link] [comments]

Is chat GPT completely illiterate when it comes to PDF and Doc files?

I've been planning an RPG and keeping a PDF file to store my work as I progress. Different sections are clearly labeled and there's really not that much content in these files yet. Maybe less than 1800 words? Chat GPT is completely blind and illiterate to instructions when it comes to pulling up information or recalling information in the document. Am I missing something? I tell it to search a word specifically and I can't find that word anywhere in the document even though it occurs multiple times. submitted by /u/Known-Presentation49 [link] [comments]

Opus 4.6 does better research, Gemini 3.1 has better judgment

Figured this out by running 4 models: Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and Grok 4.20, on a benchmark of 1,417 binary forecasting questions resolving Oct–Dec 2025 with two evaluation conditions: agentic (each model does its own web research with tools) and fixed-evidence (every model receives the same ~12k-character research dossier, compiled using the Bosse et al. 2026 standardization methodology). Note, one limitation is that the fixed-evidence dossiers are themselves LM-produced, so we may be measuring how well each model interprets a particular standardized version of the evidence rather than judgement in the abstract. But that would indicate all four models drifting in the same direction. They didn't. GPT-5.4 and Grok 4.20 barely moved between conditions while Opus and Gemini swapped rank order (the opposite of what a broken or biased eval would produce.) To my knowledge this is the first direct evaluation of frontier models that decomposes performance into these research vs judgment stages. Calibration scores, refinement scores, and per-condition analysis: futuresearch.ai/opus-research-gemini-judgment Benchmark and leaderboard: evals.futuresearch.ai Our interpretation is that Opus is dramatically better at figuring out what to search for, deciding which pages to read, and pulling out the details that matter. But when you remove research tasks, that advantage goes away. When given the same information, Gemini brings sharper judgment over fixed evidence and weights more accurately on forecasting tasks. Calibration scores corroborate this in an interesting way: Opus's calibration drops sharply when search is taken away while Gemini's actually improves with the standardized dossier,. The asymmetry suggests Opus might be using its search trace as scaffolding for probability assignment (i.e., the act of going through the search loop is itself doing some of the epistemic work, separately from the information it surfaces.) This could be an over-interpretation of one benchmark, but I'd be interested if anyone's seen the same pattern in other domains. submitted by /u/ddp26 [link] [comments]

Using Openai ads and Here's how to measure it before you spend a dollar.

OpenAI opened self-serve ads to any US advertiser. No agency, no minimum spend, no waiting list. Just register at ads.openai.com and go. Before anyone asks: CTR is sitting around 1.3% industry-wide versus 29.2% on Google Search. This is not a performance channel yet. It's a first-mover channel. The marketers who instrument it now will have real benchmarks when everyone else is still speculating in planning decks. Here's what to actually do. Step 1: Pixel before spend. OpenAI has a JavaScript pixel that ties a click inside ChatGPT to a conversion on your site. Without it you have click data and nothing else — no lead attribution, no way to know if any of this is working. Install it site-wide, fire the lead event on every demo and contact form, confirm it's working. Then and only then run spend. Step 2: Build the pipeline. The OpenAI Ads API returns performance data at four levels: account, campaign, ad group, individual ad. All four return the same response shape so one Python function handles everything. I asked Claude to write the data pull and had it running in 20 minutes. It pulls daily snapshots, runs them through Claude for a plain-language brief, and routes that to Slack every morning. Script below. Drop in your keys, schedule with cron, done. import requests import json import anthropic from datetime import date, timedelta from pathlib import Path OPENAI_ADS_KEY = "YOUR_OPENAI_ADS_API_KEY" ANTHROPIC_KEY = "YOUR_ANTHROPIC_API_KEY" BASE_URL = "https://api.ads.openai.com/v1" CAMPAIGN_ID = "YOUR_CAMPAIGN_ID" AD_GROUP_ID = "YOUR_AD_GROUP_ID" SLACK_WEBHOOK = "YOUR_SLACK_WEBHOOK_URL" # optional headers = {"Authorization": f"Bearer {OPENAI_ADS_KEY}"} today = date.today().isoformat() week_ago = (date.today() - timedelta(days=7)).isoformat() time_range = json.dumps({ "type": "date_range", "since": week_ago, "until": today }) FIELDS = ["impressions", "clicks", "spend", "ctr", "cpc", "cpm"] def pull(endpoint, extra_params={}): params = { "time_granularity": "daily", "time_ranges[]": time_range, **{f"fields[]": f for f in FIELDS}, **extra_params } r = requests.get( f"{BASE_URL}{endpoint}/insights", headers=headers, params=params ) r.raise_for_status() return r.json() data = { "account": pull("/ad_account"), "campaign": pull(f"/campaigns/{CAMPAIGN_ID}"), "ad_group": pull(f"/ad_groups/{AD_GROUP_ID}"), "ads": pull( f"/campaigns/{CAMPAIGN_ID}", { "aggregation_level": "ad", "limit": 10, "sort[]": json.dumps({"field": "clicks", "direction": "desc"}) } ), } Path("snapshots").mkdir(exist_ok=True) with open(f"snapshots/{today}.json", "w") as f: json.dump(data, f, indent=2) client = anthropic.Anthropic(api_key=ANTHROPIC_KEY) prompt = f""" You are analyzing ChatGPT ad performance for a marketing team. Here is today's data across account, campaign, ad group, and individual ads: {json.dumps(data, indent=2)} Write a 5-7 line plain-language brief covering: - Account health: spend and CTR trend vs last 7 days - Best and worst performing creatives - Anything anomalous worth flagging - One specific recommended action Direct. No filler. Written for a demand gen lead who checks Slack at 8am. """ response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=500, messages=[{"role": "user", "content": prompt}] ) summary = response.content[0].text print(summary) # Uncomment to route to Slack # requests.post(SLACK_WEBHOOK, json={"text": f"*ChatGPT Ads Daily Brief*\n{summary}"}) submitted by /u/Avem1984 [link] [comments]

I gave Claude the ability to search 18M podcast moments and transcribe my own audio (free MCP server, no credit card)

Hey r/ClaudeAI 👋 Last update post here (6 months ago) was search-only. The big change since: Claude can now transcribe your own audio through the MCP, and the free tier no longer needs a credit card. Quick rundown of what's actually different. What it does now, from inside Claude / ChatGPT / Cursor / any MCP client: → Transcribe your own audio. Drop in a recording, an interview, a meeting. Claude transcribes it (with diarization) and adds it to your private workspace. You can then search your own content alongside the public podcast index, same tool calls. This is the new piece since the last post. Screenshot showing Claude Desktop using Audioscrape MCP to transcribe Neil Armstrong's two-line transmission as he descended the ladder and stepped onto the lunar surface → Search across 18M+ podcast moments by topic, speaker, or semantic meaning. Get back timestamped quotes Claude can cite directly. Example: "Find moments where Huberman talks about cold plunges" returns 4-5 actual clips with start/end timestamps. (Last update I framed this as "1M+ hours". Same corpus, just counting searchable moments instead of hours.) → Share moments. Generated clip links unfurl with proper OG previews on Twitter/Slack/LinkedIn. Useful when you want to actually share what your AI found. What changed since the last post: • transcribe_audio is now a real MCP tool. Meetings, interviews, anything you upload. (Last post I teased "expanding beyond podcasts soon" - this is that.) • Free tier - 30 transcription min/mo, MCP integration, search across the full 18M-item index. No credit card. • Pricing actually clear now: Free / $35 Basic / $129 Pro / $299 Enterprise. Numbers, not vague tiers. • Corpus continued to grow. Deep podcast crawl across thousands of feeds, plus your own uploads in private workspaces. Try it: Sign up: audioscrape.com (free, no card) Setup snippet for Claude Desktop / Cursor: audioscrape.com/docs/claude-setup (one-line config) Ask Claude something audio-shaped I built this solo. Bootstrapped, no investors, no roadmap pressure. The whole reason for the free tier is I want indie devs and AI tinkerers to actually use it. Reddit feedback last time gave me the right direction. Happy to answer questions about how the MCP server is built, why the corpus is what it is, or anything else. AMA-ish. - Lukas submitted by /u/Lukaesch [link] [comments]