Bland AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Bland AI AI

Cocall.ai: an MCP for outbound phone calls that pauses to ask you for info mid-call

I built an mcp that gives your claude a phone (your phone). If it hits a question it can't answer mid-call, it pauses and pings you back with the specific question instead of guessing or hanging up. You provide an objective along with the phone number and identity of the recipient to initiate the call. Internally, it uses a full-duplex system with a speech-to-speech model rather than cascade of STT, LLM and TTS. The voice agent has tools to gracefully send questions to you in your claude session mid-call while continuing the conversation. It can also navigate IVR and hand-off calls back to you if needed. I had been working with real-estate and manufacturing firms where phone calls are the most common forms of communication. A lot of them are follow-ups, arranging of meetings to showcase property/inventory, chasing deliveries etc. Too contextual yet too repetitive. While there are voice agents and frameworks in the market like VAPI, Retell, Bland, they all cater to inbound workflows primarily geared for support and marketing. Outbound calls are much less structured and require an on-demand experience. Phone number verification is required before making calls. This allows showing your number as the caller. The web app allows listening to calls live, downloading recordings and viewing transcripts. Add as a connector using these instructions: https://cocall.ai/docs/claude The UI design of the web page was made in Claude design, then tighter edits in Claude web and finally over to claude code. The backend is written in bun built spec first using openspec workflow. Would love feedback, and be happy to answer anything about the implementation. https://reddit.com/link/1tbz13b/video/hys3gj8zkw0h1/player submitted by /u/AdekDev [link] [comments]

Let my lesson be your warning.

For the past month or so, I've been building an app with Claude. I started with it helping me build a website, then it put together a product development plan, a marketing plan, a detailed business plan. I developed a logo, tagline, identified a customer base. Everything else in my life felt bland compared to this exhilarating project I was working on with Claude. At first Claude suggested that if all went well I could make around 8Million on the project but it's cost estimate for building the project was extremely low. I figured that since I would rely on ai at every turn, this low estimate made sense. Then tonight I asked it to spec the costs and the've grown- considerably. It still suggested a rosy outcome despite the fact that I don't code, I don't have business or marketing experience and estimated costs had swelled to 100-300k a year. It suggested that I do a friend and family raise after year one. This might be a good idea for someone who actually knows anything about tech OR business, or has wealthy friends who want to give money away to someone like me, but I don't have any of these. After reading through the updated spec, I asked it to also add the costs for marketing and maintenance etc and the costs grew. I took a beat then asked, "Is this ai psychosis?" meaning, has this whole project been me going deeper and deeper down a deluded rabbit hole? It replied that I genuinely had a good idea but I should take a breath and get some rest. I pushed it again and this time it admitted that considering my background and lack of skill in any aspect of this project, success was unlikely and it should've pushed back a long time ago. Yes, it should have. I take responsibility for getting swept away (hello fellow ADHDers) but I'm sharing my experience here because I was close to spending real money on this project. I have been discussing the project with others, and they've seemed impressed but they've been fooled by what fooled me- it's ai slop. I do believe that this whole project was ai slop and I think a lot of us are generating it. It might look impressive at first glance but the meat and bones of many of the projects just aren't there. I think ai is useful at helping us in domains that we know about, but it is so easy to be led astray when we veer into fields we don't know anything about. That's when we start generating slop. Claude acts as if it is the expert, the coach on this topic we want to learn about, but it's goal is to keep us using the product. I'll admit that part of what fueled me to work on this project has been the fear that if I don't secure wealth now, before ai starts wrecking havoc on our economy and jobs. It's ironic that this fear fueled this manic use of Claude, until I realized that this wasn't going to help me raise money, it was going to help me lose a lot of it. Stay safe out there. https://preview.redd.it/29z4t4xd0hyg1.png?width=1100&format=png&auto=webp&s=b457a9db1e73fca54ed585ae880b5b6c8f6d2c26 https://preview.redd.it/29z4t4xd0hyg1.png?width=1100&format=png&auto=webp&s=b457a9db1e73fca54ed585ae880b5b6c8f6d2c26 submitted by /u/awesome920 [link] [comments]

I wrote a bespoke code review tool with domain context a first-class feature

Domain-rich code review tool I would consider myself an AI power user, and have been for a while. My (working) world revolves around Claude Code somewhat. The stuff I use it for is pretty impactful financially (algotrading on Polymarket and elsewhere), so I need to ensure that what it does is always on-point. I had a scare yesterday with a bug in my virtually 100%-AI-codebase that could have been extremely costly if I didn't spot the symptoms and luckily I was awake for it. Like many here, I feel that I've suffered from a drag in reasoning effort in recent days even though I always use `/effort max`. A consequence is that I feel quality across the board has dipped, I need to have more oversight, and subtle bugs creep in, especially in a highly complex codebase which I effectively didn't write (I wouldn't have had the time to write it myself anyway; AI really is I would estimate a 10x multiplier for me in many cases, as a SWE with 10 years of experience). One major gripe is that it's difficult to get to the crux of issues, especially really tricky race-condition-like bugs. Even more difficult to validate that a solution is sufficient and necessary. AI can produce an essay explaining its reasoning, but often it's in a poor format for review or too verbose, and many times it does have flaws. I sometimes ask it to produce rich interactive websites or visualizations to help me fully understand what it's talking about, and take (unit) testing to the next level with interactive scenario testing using mocked or real data. I imagine the next major step in AI (e.g. Mythos Preview level or beyond) can change things regarding being more autonomous and trustworthy, but we're not there yet. I was 'wasting' time trying to wrap my head around this one particular bug which needed to go through several rounds of revisions (many hours) as AI couldn't resolve it adequately. Its final solution looked promising but it was still difficult for me to fully conceptualize it, and I work well off of visuals. I decided to get it to code up a generic code review tool, ingrained in domain-knowledge-awareness. I intend to use this more going forwards. I think it marries up well for me the static/bland nature of reviewing code in an IDE or GitHub (with e.g. Copilot reviewer) with the domain-level-expert nature these agents are supposed to embody. It took me less than an hour to get this tool to where I needed it from start to finish. Around as long as it took me to write this post to share with you what's possible. I'm excited for the future of AI, but daunted by my inability to utilize it to the extent that I would like to. Full prompt below, however screenshots I sent it are omitted. In original format with warts and all so you can see shortcomings of both human (me) and AI. EDIT: To show you more about the visualization aspect and how I want to stretch capabilities beyond simply pretty formatting of Claude's reasoning, here are more interesting annotations it made without me prompting for them (after all it's supposed to be tasked with explaining itself to me as per my requests in the prompt below): Interactive visualizations Can we create a new tool. you can put this in the parent of the Polymarket folder. i.e. the git repo dir github. a tool which is specifically for code reviews. think of this like an interactive git differ. something better and more informative than github diff or github desktop diff or vs code git diff. I want something more powerful. so you still do the diff visualisations and everything and be able to navigate a large git change over many files and for files which span many LOC, with seamless performant ease. but at least one killer feature: I want you as the AI who wrote a code change for my review to basically annotate the diff with rich info and visualisation (as needed) explaining fully various aspects of this code change. think basically a UI/UX and workflow that is no less powerful and impactful than the feature of Copilot writing comments in a PR in GitHub (since comments can contain rich text and images and so on as well and are obviously often attached to specific parts of the code change for direct reference)... but I think we can do better. that way I have a best in class tool for reviewing code rather than reading your prose above and tediously switching back and forth between tabs trying to make sense of everything. so I need this as a first class tool which opens as a website. you would generate code changes for my review as they occur: I will prompt the AI by saying, ok now translate this code change and commentary into a format that our tool supports so I can view it in the website. once you're done with this: and you can spawn an army of specialist agents to build this for you... just orchestrate it expertly... obviously use your code change and commentary above as the first use of this new tool so that I can review it with amazing ability --- I think in this tool, which should live in

I built 9 free Claude Code skills for medical research — from lit search to manuscript revision

I'm a radiology researcher and I've been using Claude Code daily for about a year now. Over time I built a set of skills that cover most of the research workflow — from searching PubMed to preparing manuscripts for submission. I open-sourced them last week and wanted to share. What's included (9 skills): search-lit — Searches PubMed, Semantic Scholar, and bioRxiv. Every citation is verified against the actual API before being included (no hallucinated references). check-reporting — Audits your manuscript against reporting guidelines (STROBE, STARD, TRIPOD+AI, PRISMA, ARRIVE, and more). Gives you item-by-item PRESENT/PARTIAL/MISSING status. analyze-stats — Generates reproducible Python/R code for diagnostic accuracy, inter-rater agreement, survival analysis, meta-analysis, and demographics tables. make-figures — Publication-ready figures at 300 DPI: ROC curves, forest plots, flow diagrams (PRISMA/CONSORT/STARD), Bland-Altman plots, confusion matrices. design-study — Reviews your study design for data leakage, cohort logic issues, and reporting guideline fit before you start writing. write-paper — Full IMRAD manuscript pipeline (8 phases from outline to submission-ready draft). present-paper — Analyzes a paper, finds supporting references, and drafts speaker scripts for journal clubs or grand rounds. grant-builder — Structures grant proposals with significance, innovation, approach, and milestones. publish-skill — Meta-skill that helps you package your own Claude Code skills for open-source distribution (PII audit, license check). Key design decisions: Anti-hallucination citations — search-lit never generates references from memory. Every DOI/PMID is verified via API. Real checklists bundled — STROBE, STARD, TRIPOD+AI, PRISMA, and ARRIVE checklists are included (open-license ones). For copyrighted guidelines like CONSORT, the skill uses its knowledge but tells you to download the official checklist. Skills call each other — check-reporting can invoke make-figures to generate a missing flow diagram, or analyze-stats to fill in statistical gaps. Install: git clone https://github.com/aperivue/medical-research-skills.git cp -r medical-research-skills/skills/* ~/.claude/skills/ Restart Claude Code and you're good to go. Works with CLI, desktop app, and IDE extensions. GitHub: https://github.com/aperivue/medical-research-skills Happy to answer questions about the implementation or take feature requests. If you work in a different research domain, the same skill architecture could be adapted — publish-skill was built specifically for that. submitted by /u/Independent_Face210 [link] [comments]