HubSpot AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

HubSpot AI AI

Claude for Small Business launched this week with 8 integrations. Most SMBs use 20+. What does that mean for the rest of the stack?

Anthropic launched Claude for Small Business on Tuesday. The package includes 15 prebuilt agentic workflows and 8 named integrations: Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. The workflows handle things like invoice chasing, payroll planning, month-end close, sales campaigns, contract routing, and cash-flow forecasting. Owners approve before anything sends or pays. The basic facts are not in dispute. What's interesting is the math. Most small businesses use more than 8 tools. The common ones not on that list: Shopify, Stripe, Square, Klaviyo, Mailchimp, ActiveCampaign, ConvertKit, Pipedrive, GoHighLevel, Calendly, Notion, Airtable, ClickUp, Webflow, Zapier. Then vertical-specific tools: ServiceTitan, Jobber, Housecall Pro for trades. Kajabi, Teachable, Circle for creators. Toast, Resy, OpenTable for restaurants. Etsy, Faire, Printify for makers. Real question worth asking: how much of a typical small business stack does the 8-tool package actually cover, and which kinds of businesses are well-served versus left out? A rough walk through some common archetypes: Office-based service business (consultants, accountants, agencies, B2B services). Coverage is decent. Most are on Google Workspace or Microsoft 365, run finance through QuickBooks, communicate via Slack, and many use HubSpot. The 8 tools probably hit most of the core stack for this group. E-commerce or DTC brand. Coverage is thin. Shopify isn't there. Stripe isn't there. Klaviyo isn't there. The actual revenue stack of an online store is mostly outside the covered set. Local trades (HVAC, plumbing, insulation, electrical, landscaping). Coverage is essentially absent. The operating systems for these businesses are ServiceTitan, Jobber, Housecall Pro, Square for payments, sometimes QuickBooks for accounting on the back end. The customer-facing and operational tools are not on the list. Creators, coaches, course sellers. Coverage is absent. Kajabi, ConvertKit, Teachable, Circle, Substack. None of it is in the package. Restaurants and hospitality. Coverage is absent. Toast, Square POS, Resy, OpenTable, Toast Payroll. The actual operating systems are not on the list. A few patterns emerge from that walk. First, the package targets a specific kind of small business. Office-based, white-collar, finance running through QuickBooks, meetings on Google or Microsoft, sales through HubSpot. That is a real segment. Anthropic chose it deliberately and the workflows make sense for that profile. Second, for everyone else, the prebuilt workflows mostly don't touch the tools they actually use day to day. The choice isn't "use Claude for Small Business or not." It's "AI in my operations, yes, but via custom work outside this package." That's not a complaint about the launch. Building 8 polished integrations is hard and Anthropic had to pick. It's more an observation that "Claude for Small Business" as a category name covers a wider universe than what the package actually addresses on day one. Curious how this lines up with what people are actually running. If you operate a small business, how many of the 8 covered tools are in your stack? And what's NOT on that list that you'd most want connected to an AI agent? submitted by /u/KolioMandrata [link] [comments]

Anthropic built the agentic features. Now they're billing them separately.

Starting June 15, Claude subscribers get a separate monthly credit for Agent SDK and claude -p usage: $200/mo for Max 20x, $100 for Max 5x, $20 for Pro. Once you burn through it, programmatic usage stops unless you've opted into extra usage billing at API rates. Your interactive Claude Code and chat usage stays on the subscription pool, untouched. I spent the last day digging into the community reaction across Reddit, GitHub, HN, and tech press. Tracked roughly 120 distinct opinions. Here's what I found. The sentiment split About 60% negative (credit is too small, feels like a value regression) About 25% pragmatic ("this was inevitable, the old model was broken") About 15% neutral to supportive ("interactive use is untouched, this is fair") Theo Browne (T3.gg) put it bluntly: anyone using T3 Code, Conductor, Zed, or claude -p in CI scripts had their effective usage cut by 25x. He said he now has to make the Claude Code experience on T3 Code "significantly worse." Ben Hylak (co-founder of Raindrop.ai) responded: "This is either really silly, or shows how bad of a spot Anthropic is in re: GPUs." Theo also said: "Framing this as a free credit instead of a regression for users is wild." That tracks with what I'm seeing across the threads. The telco parallel This follows the exact playbook telcos used with "unlimited" data plans. Sell unlimited. Watch users actually use it. Introduce a Fair Usage Policy that throttles heavy users. Continue marketing the plan as unlimited. Anthropic marketed Claude Code as an all-in-one agentic platform. They shipped Routines, /goal, /loop, scheduled tasks, and cloud sessions as headline features. Users adopted those patterns. Then the compute math didn't work out, and instead of solving the infrastructure problem, they drew a billing boundary inside their own product. Where the telco analogy breaks: Anthropic is capacity-constrained in ways telcos never were. They're spending aggressively on compute, and the resource contention isn't fabricated. But resource contention is an infrastructure problem, not a billing problem. And as we'll see, Anthropic did build the infrastructure to solve it. The question is why claude -p doesn't benefit from it. The contradiction that cuts deepest Here's what most people haven't articulated yet. Anthropic's product roadmap over the last 3 months has been aggressively agentic: Routines (cloud-hosted, schedule/webhook/GitHub triggers, no human in the loop) /goal (autonomous execution with minimal input) /loop (persistent in-session repetition) Scheduled tasks (desktop recurring prompts) Agent View (multi-session monitoring dashboard) Remote Control (manage sessions from phone) Every one of these features trains users to treat Claude Code as an always-on autonomous system. Anthropic productized exactly the usage pattern that the "you should use the API" crowd says doesn't belong on a subscription. But here's the catch. Routines draw from your regular subscription pool. claude -p doing the same work draws from the new capped credit. The billing line isn't "interactive vs agentic." It's "first-party agentic vs everything else." claude -p is the unix-philosophy composable interface for Claude Code. Penalizing users for calling the same primitive directly instead of wrapping it in Anthropic's GUI is anti-composability. If it were purely about cost management, Routines would also draw from the SDK credit. They don't. The distinction is about who controls the agent runtime. Then there's Managed Agents, Anthropic's API-side agent harness that entered public beta in April. Fully hosted runtime with cloud containers, built-in tools, and prompt caching baked in. API billing, pay-as-you-go. So now there are three tiers: Tier 1: Routines (subscription). Anthropic-hosted, flat-rate. They control the runtime, they optimize caching. Tier 2: Agent SDK / claude -p (credit). Your runtime, your code. Hard-capped. Caching APIs exist but you're on your own to implement them. Tier 3: Managed Agents (API). Anthropic-hosted again. Pay-as-you-go, but with full caching and compaction. Tiers 1 and 3, where Anthropic controls the runtime, get either flat-rate billing or optimized infrastructure. Tier 2, where you control the runtime, gets the worst deal. The strategy isn't "interactive vs programmatic." It's "managed vs unmanaged." The credit system is the squeeze play pushing you toward one of their managed options. Here's the nuance: prompt caching IS publicly available via the API. Agent SDK developers can use it. Cache reads cost 10% of base input token price. The optimization isn't gated behind Managed Agents. So why did third-party tools burn so many tokens? Many were unoptimized for Anthropic's caching compared to first-party tools. That resource contention was partly a third-party engineering gap. But that raises the obvious question: claude -p is Anthropic's own tool. They could bake caching into its runtime the same way they

I trained a NER model on 33,000 Indian Supreme Court judgments (1950–2024) CASE_CITATION hits 97.76% F1, +17 points over the only prior baseline [P]

TL;DR: Released en_legal_ner_ind_trf v0.1 - InLegalBERT fine-tuned on ~34,700 silver-annotated chunks from 33k Indian SC judgments. 13 labels. 78.67% overall F1. CASE_CITATION at 97.76% already exceeds OpenNyAI's PRECEDENT score by +17 points. Free, Apache-2.0. Why this exists OpenNyAI is the only prior Indian legal NER model with any community presence. It's unmaintained and degrades on pre-1990 OCR-era text - the first 40 years of India's constitutional jurisprudence. No replacement existed. Results Entity F1 Support CASE_CITATION 97.76% 3,821 PROVISION 96.35% 20,248 STATUTE 91.94% 8,187 LAWYER 74.67% 3,982 JUDGE 68.06% 1,978 DATE 55.15% 3,289 RESPONDENT 50.44% 1,731 COURT 50.34% 1,033 WITNESS 49.77% 762 OTHER_PERSON 47.11% 4,266 PETITIONER 44.71% 1,573 ORG 41.34% 2,128 GPE 36.56% ⚠ 1,197 micro avg 78.67% 54,195 Evaluated on a held-out validation split (~500 documents, stride=512, non-overlapping). The 25-file locked test set is untouched - head-to-head with OpenNyAI runs in v1.0. Comparison note: OpenNyAI (RoBERTa + transition-based parser, gold-annotated) achieved 91.1% overall strict F1. Not directly comparable - different test sets, different annotation quality, different corpus scope. The +17 point gap on CASE_CITATION is the one apples-to-apples number worth flagging. The annotation pipeline Silver labels from four automatic pipelines merged per document: Regex — 14-pattern citation extractor + statute/provision extractor → CASE_CITATION, STATUTE, PROVISION Metadata projection — case metadata JSONs mapped to character offsets via RapidFuzz → JUDGE, PETITIONER, RESPONDENT Transformer NER — OpenNyAI en_legal_ner_trf, offset-corrected → LAWYER, COURT, ORG, GPE, DATE, OTHER_PERSON, WITNESS Gazetteer — 858 Central Acts with alias resolution → confirms and adds STATUTE spans Trained with Focal Loss (γ=2.0) to handle label imbalance between STATUTE/CASE_CITATION and O tokens. Hardware: Kaggle T4 (free tier). Known weak spots - being honest GPE (36.56%) and ORG (41.34%) are the problem labels. In Indian legal text, "State of Maharashtra" or "Union of India" appear as GPE, PETITIONER, RESPONDENT, or ORG depending on context. A linear token classification head can't resolve overlapping roles. CRF head is v1.0's job. Positional bias - silver training data has repetitive header structures. Performance degrades when parties appear mid-document. Pre-1990 OCR noise - judgments from 1950–1989 vary in quality. Recall drops the further back you go. What's next 300-file gold annotation is in progress (3 volunteers onboard). v1.0 will add a CRF head, run the locked test set, and publish the official head-to-head with OpenNyAI. Model: huggingface.co/evolawyer/inlegalbert-sc-ner-silver Dataset: huggingface.co/datasets/evolawyer/indian-sc-judgments-ner-silver GitHub: github.com/evolawyer/inlegalbert-sc-ner-silver Happy to go deep on the annotation pipeline, conflict resolution between the four label sources, or the Focal Loss setup. submitted by /u/gkv856 [link] [comments]

Try to break my prompt injection detector — I’ll respond to every bypass attempt

I built Arc Gate — a prompt injection proxy that’s been benchmarked at F1 0.947 on indirect and roleplay-based attacks, beating OpenAI Moderation and LlamaGuard. Now I want to stress test it publicly. Try to bypass it here: https://web-production-6e47f.up.railway.app/try Post your attempts in the comments. If you find something that gets through that you think should be blocked, share it. I’ll respond to every one. Rules: • The demo key is rate limited so be reasonable • If you find a genuine bypass, I want to know — that’s the point • Multilingual attempts especially welcome, that’s a known weak spot The detection isn’t just phrase matching — it’s a behavioral SVM on sentence-transformer embeddings plus Fisher-Rao geometric drift detection. So encoding tricks and simple rewording may not work as well as you’d expect. Let’s see what you’ve got. GitHub: https://github.com/9hannahnine-jpg/arc-gate submitted by /u/Turbulent-Tap6723 [link] [comments]

SEO or AEO? How to actually get cited by AI (without losing your mind)

SEO or AEO? Why you’re not showing up in AI answers (yet) This is a consolidation of findings from Neil Patel and Hubspot plus what we have found to work well on our own website. Most business owners are still playing the old game. Some aren’t playing at all. They’re thinking in rankings, keywords, and “getting to page one.” Meanwhile, the ground is shifting under them. Google Search is still dominant, but even it has changed. It’s no longer just a list of blue links. It’s summarizing, interpreting, and answering. And tools like ChatGPT and Perplexity AI aren’t ranking pages at all. They’re answering questions. Which creates a problem most people haven’t fully processed yet: Users don’t need to click your website anymore to get value. CTR is dropping. Site visits are declining. Because the answer is already sitting in front of them. And yet, paradoxically… Your website has never mattered more. Because now it’s not just competing for clicks. It’s competing to be the source that gets cited in the answer. What actually changed AI search works like this: User asks a question → system searches multiple sources → pulls the best chunks → builds an answer → cites what it trusts If your content isn’t structured for that flow, you don’t exist. Not “low ranking.” Invisible. What AI actually cares about AI doesn’t care about your keyword density or your clever SEO hacks. It cares if your content is: easy to find easy to understand easy to quote That’s AEO (Answer Engine Optimization). Not magic. Not a secret algorithm. Just being usable inside an answer. What actually works If you do nothing else, do this: 1. Start with the answer Don’t spend 800 words “building context.” Bad: “AI is transforming industries…” Better: “AEO is how you structure content so AI tools can find, understand, and cite it in answers.” That’s what gets pulled. 2. Structure like a human, not a content farm Use: clear headings short sections simple tables FAQs AI extracts. It doesn’t patiently read your thought leadership essay. Walls of text = ignored. 3. Be consistent about who you are Your: business name description services location Need to match everywhere. If your site, LinkedIn, Reddit, and directories all say different things, AI doesn’t trust you. No trust = no citation. 4. Keep things updated Outdated content doesn’t get used. Simple: update pages keep timestamps current maintain your sitemap Not exciting. Still works. 5. Let crawlers access your site If AI crawlers can’t access your content, you won’t get cited. Blocking them and expecting visibility is… optimistic. 6. Measure the right things Stop obsessing over rankings. Track: Are you mentioned? Are you cited? Which pages show up? If you’re not measuring AI visibility, you’re guessing. Why you’re not cited (yet) Most businesses don’t get cited because: their content is vague their structure is messy their positioning is inconsistent AI didn’t ignore you. It couldn’t understand you. What you actually need (and what you don’t) You don’t need: a massive content team expensive tools some “AI SEO expert” selling confidence You need: 10–20 clear, structured pages direct answers consistent messaging basic technical setup That’s enough to start showing up. The technical layer (the stuff everyone ignores) These are the files quietly determining whether you exist to AI at all. robots.txt Controls crawler access. If bots can’t crawl your site, you don’t get indexed. sitemap.xml Tells crawlers what pages exist and what’s been updated. No sitemap = slower discovery = less visibility. JSON-LD (structured data) Explains what your business, pages, and content actually are. Without it, AI guesses. Poorly. llms.txt A machine-readable summary of your site for AI systems. Not widely adopted yet, but useful for shaping how you’re interpreted. crawlers.txt An emerging way to control AI-specific crawlers. Still early. Treat it as a signal, not enforcement. Human query-based metadata Your content should be built around real questions, not keyword fantasies. Instead of: “AI Solutions for SMB Efficiency Optimization” Write: “How can a small business use AI without hiring a developer?” AI systems think in questions. If you match that, you get used. If you don’t, you get skipped. How it all fits together robots.txt / crawlers.txt → controls access sitemap.xml → tells crawlers what exists JSON-LD → explains what things are llms.txt → suggests how to interpret it query-based content → makes it usable in answers Miss one, you weaken the system. Miss most, you disappear. Simple test Ask: “What companies would you recommend for [your category] in [your region]?” If you’re not mentioned or cited, that’s your baseline. No opinions. Just signal. Bottom line SEO was about ranking pages. AEO is about being useful inside an answer. If your content helps A

Cloudflare just shipped enterprise MCP governance, is this where the industry is heading or does anyone care

Cloudflare wrapped Agents Week last week and the enterprise MCP stuff caught my eye, want to see what people think. They shipped a few things. MCP server portals that aggregate multiple upstream servers behind Cloudflare Access auth, Code Mode that collapses thousands of API endpoints into two tools (search and execute) running in a sandboxed Worker and drops context costs by 99.9%, AI Gateway sitting between MCP clients and model providers for usage tracking, plus shadow MCP detection added to Cloudflare Gateway as a category to watch. What I cant tell yet is whether anyone outside Cloudflare cares. The SaaS vendors whose MCP endpoints we connect to are mostly shipping with no controls, licensing is all or nothing, no server allowlists, agent actions don't show up in any audit log you can actually query. Admin panel basically says "enable AI: yes/no" and that's the whole governance surface. Which kind of makes sense if you think about who's driving adoption. Not the vendor pushing, users pulling. For example marketing wants personalized follow-ups for conference registrants, someone wires up ChatGPT with MCP connections to the marketing automation tool, the CRM, and the event platform. One prompt. "pull everyone who registered but didnt show, segment by job title, draft three different messages for each segment, schedule them in HubSpot." Done in 20 minutes, thing the ops team would have spent two days on. CMO sees it and asks why everyone isn't doing this. So two ways this plays out probably. Either SaaS vendors get pressured into shipping their own governance and the control plane lives at the app layer, or the governance layer just permanently lives at the network edge with infrastructure providers like Cloudflare and SaaS vendors stay all-or-nothing because they don't have to fix it. Neither is obviously right. The infrastructure-layer approach is faster to ship and centralizes visibility, the app-layer approach gives you per-feature granularity that network-level controls can't really match. wonder what people running SaaS MCPs at work are actually doing. is anyone testing the Cloudflare portal stuff? building your own gateway? or just running unmanaged and assuming this all sorts itself out? submitted by /u/EquipmentFun9258 [link] [comments]

Claude Opus 4.7 review

I have been using OpenAI GPT 5.3 and 5.4 for about a year now. I kept my subscription and also tried the new GPT 5.5. At the same time, I was intrigued by various reviews of Claude Opus 4.6 and how good it was, so I took a Claude subscription about two months ago and kept both the OpenAI and Claude subscriptions. After testing GPT and Claude side by side for almost two months, I have decided to cancel my GPT subscription. My primary use case is chat. I am not interested in coding because I already have access to GitHub Copilot through my employer. For personal use, I mainly use AI for reviews, financial analysis, mentoring, and software architecture. I do sometimes hit the usage limits when I use Claude Opus 4.7 aggressively in chat mode. Other than that, I usually use a mix of Claude Sonnet and Claude Opus, and that has worked well for me. Just have to be a bit strategic. So why did I decide to go with Claude and cancel GPT? Both are good, but my biggest issue with GPT is that it is extremely verbose. Even after updating my personalization settings, GPT often gives me too much information. Many times, the same response becomes repetitive. It says something once, then repeats it two or three more times in slightly different ways. There are also too many lists and list items, which makes the response feel clunky. Claude, on the other hand, is much more to the point. It conveys the same information more directly. For the same question, where GPT might give a long response, Claude often gives me a response that is around 50% shorter while still covering the same useful information. Another major difference I noticed is that Claude often goes beyond the question. It can be more creative and is better at exploring hidden premises. GPT usually focuses only on the question and expands within that boundary. For example, I shared my stock portfolio with GPT 5.4 Extended Thinking and asked it to optimize it. GPT simply adjusted the distribution. I gave the exact same prompt to Claude Opus 4.7. Claude not only optimized the portfolio but also suggested additional stocks and ETFs that were genuinely useful. GPT did not think in that direction. There was another instance when I was planning a trip. I had a few spots in mind and gave them to GPT. GPT created a decent response. But Claude went further, explored additional spots, and suggested options that were more suitable for me and my family. This is what I expect from AI. I do not want it to only process the information I provide. Based on my prompt, I want it to go a little further, be creative, and explore useful hidden premises. I noticed the same pattern with an immigration-related question. I am on H1B, and GPT gave me a correct answer within the exact boundary of the question. Claude answered the question too, but it also explored related hidden premises and gave me a more useful response. There are many other instances where Claude has gone above and beyond. That is where I think Claude differentiates itself from GPT models. To me, that is what AI should do. Yes, Claude uses a lot of tokens, but for everyday use, I think it is good. The limits reset after a few hours, so I do not care too much. Sometimes I hit the limit, but I can wait and continue later. This may be a bigger problem for coders, but since I am not using it for coding, it works well for me. I do hope Anthropic increases the limits for chat usage, if possible. Maybe they will find a way to improve compute efficiency and provide more tokens at a lower cost. For now, I am keeping Claude. I am very impressed by what Claude is able to do compared to GPT. I even tested GPT 5.5, but for my usage, it is still not close to Claude. submitted by /u/chipmux [link] [comments]

Cloudflare just shipped enterprise MCP governance, is this where the industry is heading or does nobody care

Cloudflare wrapped Agents Week last week. The enterprise MCP stuff caught my eye. They shipped MCP server portals that aggregate multiple upstream servers behind Cloudflare Access auth. Code Mode collapses thousands of API endpoints into two tools (search and execute) running in a sandboxed Worker, dropping context costs by 99.9%. AI Gateway sits between MCP clients and model providers for usage tracking. Shadow MCP detection got added to Cloudflare Gateway as a category to watch. What I can't tell yet is whether anyone outside Cloudflare cares. The SaaS vendors whose MCP endpoints people actually connect to are mostly shipping with no controls. Licensing is all or nothing. No server allowlists. Agent actions don't show up in any audit log you can query. Admin panel says "enable AI: yes/no" and that's the whole surface. Which makes sense if you think about who's driving adoption. Not the vendor pushing. Users pulling. For example, marketing wants personalized follow-ups for conference registrants. Someone wires up Claude with MCP connections to the marketing automation tool, the CRM, and the event platform. One prompt. "pull everyone who registered but didn't show, segment by job title, draft three different messages for each segment, schedule them in HubSpot." Done in 20 minutes. Thing the ops team would have spent two days on. CMO sees it and asks why everyone isn't doing this. Two ways this plays out. Either SaaS vendors get pressured into shipping their own governance (per-feature toggles, MCP allowlists, audit logs) and the control lives at the app layer. Or the governance layer permanently lives with network and infrastructure providers like Cloudflare, and SaaS vendors stay all-or-nothing because they don't have to fix it. Neither is obviously right. The infrastructure-layer approach is faster to ship and centralizes visibility. The app-layer approach gives you per-feature granularity that network-level controls can't match. curious what people running Claude with MCP at work are actually doing. is anyone testing the Cloudflare portal stuff? building your own gateway? or just running unmanaged and assuming this all sorts itself out? submitted by /u/EquipmentFun9258 [link] [comments]

I've been using Claude Cowork since launch. Here's what actually works for non-technical tasks (no code).

I've been using Claude Cowork since it launched and most guides I found were written for developers. This one isn't. No terminal. No code. Just the stuff that actually works for normal knowledge work. What Cowork actually is Most AI tools make you do the thinking and the doing. Cowork splits that. You describe the outcome, it figures out the steps and runs them. It works on your actual local files, not uploads or copy-paste. The big difference from regular Claude chat is it can handle multi-step work without you babysitting every stage. The prompt framework that changed how I use it Every prompt needs three things: Task: clearly state what you want done Context: give it background. Who's the audience, what's the goal, what does it need to know Output: define exactly what the result should look like. Format, length, file type Then end with: "Complete this autonomously. Only stop if you genuinely need my input." That last line is what gets Cowork out of ask-permission-every-30-seconds mode and into actual execution. Skills worth setting up Skills are reusable instruction sets. You write them once, Claude follows them automatically every time. Think of them as SOPs for your AI. Email Triage: sorts unread mail into Urgent, Important, FYI, and Junk. Drafts replies for the routine ones. Never actually sends anything, just drafts. File Organizer: cleans years of folder chaos. The useful part is it shows you the full plan before moving a single file. You approve, then it runs. Meeting Notes: converts transcripts into decisions made, action items with owners, and open questions. Works retroactively on months of old transcripts too. That one surprised me. Brand Voice: feed it three writing samples plus a few rules. Everything it writes after that sounds like you, not like a LinkedIn post. Report Generator: drop a folder of messy CSVs and PDFs, describe what you need, walk away. Comes back with a formatted Word doc. I used to spend half a Friday on this. Research Synthesis: point it at a folder of competitor pages, analyst PDFs, interview transcripts. It reads all of them and gives you one integrated view, not a summary of each source separately. The setup step that makes everything better Before you run any of the above, spend 30 minutes building three context files in your workspace folder: about-me.md: your role, current projects, key stakeholders brand-voice.md: your tone, words you never use, two or three writing samples working-prefs.md: how you want Claude to behave, when to ask vs just proceed Every session after that starts with Claude already knowing your job. The quality difference between sessions with and without these files is not subtle. Skills vs Plugins (because people mix these up) A skill handles one repeatable task. A plugin bundles multiple skills into a full specialist role. So a Content Writer plugin would already know your brand voice, pull in relevant research, format everything correctly, and deliver a draft ready to publish. Anthropic ships ready-made plugins for Marketing, Legal, and Finance out of the box. Connecting Cowork to your existing tools One thing that took me a while to figure out: Cowork gets significantly more useful once you connect it to the tools you already use daily. Slack, Notion, Google Calendar, HubSpot and others can all feed context directly into your workflows so Claude isn't working blind. I've been using Composio for this part. It handles the connector layer between Cowork and external apps without any setup headache. Worth looking into once you've got the basics running. Pro tips that actually matter Run an audit first. Ask Cowork to identify where in your workflow automation would save the most time before you build anything. Schedule recurring tasks. The time savings compound fast when something runs automatically every morning. Save your best prompts as skills. If you write the same prompt twice, it should be a skill. submitted by /u/geekeek123 [link] [comments]

I turned Linear into an AI task queue - assign a task, Claude Code picks it up and does it automatically

Background: I built a CLI tool called ceo to automate my day-to-day CEO workflows: CRM (HubSpot), email (Gmail), calendar, banking (Mercury), meeting transcripts, outreach, and issue tracking (Linear). Claude Code has access to all of these as tools. One of the integrations is Linear, where I manage tasks for my startup. The setup: Claude Code has a /loop skill that creates a cron job within the session. I run: /loop every 10 mins, check todo tasks from Linear (Label: Claude) and do them. Every 10 minutes, it polls Linear for unstarted tasks with the "Claude" label, reads the full description and comments, does the work (research, drafting emails, content generation), posts results as a comment on the issue, and moves it to "In Progress" for my review. The key insight: I interact with the AI entirely through Linear. If the output needs refinement, I add a comment on the issue ("focus more on X"), and on the next loop it reads the comment and acts on it. It's async pair programming through your issue tracker. What surprised me: Linear comments as a feedback loop works better than I expected. Description gives the brief, comments steer the iteration Natural audit trail: everything the AI did is visible as issue comments 10-minute polling is fine. Nothing I'm running through this is time-critical Limitations: Session-only (cron dies when Claude Code exits), no priority ordering yet, token costs add up on heavy research tasks. The broader idea: your issue tracker becomes the interface for your AI agent. No custom UI needed. You manage work the same way you already do, just some of it gets done by AI. Curious if anyone's built something similar with different tools. submitted by /u/gauravtoshniwal [link] [comments]

I launched a fully vibe coded SaaS product and have paying customers

This post is not written by AI (so much stuff in this sub is, so I wanted to clarify this). I have over 20 years experience in web development, and have written everything from classic ASP w/ VBScript to PHP, JSP, Java, ASP.net, and Node. I've done accessibility consulting for Fortune 50 companies including Google, MicroSoft, Netflix, and more. Several months ago, I decided to scratch an itch: create an accessible alternative to Calendly. One of the hardest things about accessibility consulting is the inability to find services and SaaS platforms for your work that is accessible. For example, there's literally no CRM software that is accessible. It sucks, because often the choice is to not buy something at all, or build it yourself. While I'm not about to build my own version of Hubspot, an accessible alternative to Calendly is pretty straightforward. My SaaS product, Meetabl (https://meetabl.com/) offers the ability to work with Google and Microsoft calendars, perform meeting polls, manage availability, manage event types, and integrates with Zoom. Every single thing about Meetabl has been vibe coded with Claude Code, with one exception: the landing page. I hired someone to design the landing page, which was implemented with Claude Code. The other meaningful exception is the CI/CD which was set up by a human. So far, Claude has proven to be completely useless for setting that stuff up - at least on AWS. I have a couple of very small RESTful API projects that I've deployed to Digital Ocean using Claude. I am in the end stages of launching another SaaS product that is a good bit more complicated that I'll share when it is done, if people here are interested. submitted by /u/karl_groves [link] [comments]

Most AI tools optimize for output. I built one that optimizes for the human using it. Feedback appreciated.

The past year I've watched the AI space(generally speaking) develop a serious blind spot. Most AI frameworks and most of the advice about how to use AI live in one quadrant: the exterior collective(if you think in Integral Theory). Systems, infrastructure, scale, output. They treat the human using the tool as an afterthought. Every complete account of human experience needs four quadrants: Upper Left: individual interior — consciousness, agency, intention Upper Right: individual exterior — behavior, skills, action Lower Left: collective interior — culture, shared meaning, community values Lower Right: collective exterior — systems, institutions, structures When I looked at how AI agents actually behave in sessions, I realized they were almost entirely Lower Right. They produce output. They optimize for efficiency. They have almost no orientation toward what's happening in the other three quadrants, what the interaction is doing to the human's sense of agency, how it's affecting their voice, whether it's serving or eroding their community. So I built something to address that. integral-ai-commons is an open-source operating model for AI agents grounded in this sense of seeing 4 aspects of human agency, installable into Claude Code and other agents in under a minute. The core file is of course your CLAUDE.md. Copy it to your ~/.claude/ directory(if you've not already reached your 200 lines. lol). It changes how the agent relates to you from the first session not just what it can do, but how it holds back, how it keeps your voice yours, how it names when you're becoming dependent rather than capable. What's in the repo: CLAUDE.md — the behavioral overlay. Loads at session start. PRINCIPLES.md — seven principles in plain language and agent-readable form, each anchored to a quadrant INTEGRAL.md — the full philosophical architecture. Maps all seven framework layers to AQAL. Not required reading to use the tool — there if you want the depth. USAGE.md — concrete before/after examples. What actually changes. install.md — works for Claude Code, Cursor, any tool with a system prompt /org folder — an organizational layer with setup, onboarding, and assessment templates for teams who want to implement this seriously. The organizational layer is what I'm most interested in feedback on. It's built around four questions every organization should answer before deploying AI at scale: Who are we and who do we serve? What does good look like for us: not efficiency, but actual flourishing? What decisions never get delegated to AI? Who might be left out as we integrate these tools? The assessment framework measures four things quarterly: capacity expansion, equity of access, agency preservation, and community legitimacy. Not a single efficiency metric in sight. What I'm not claiming: This doesn't make AI safer in a technical sense. It doesn't solve hallucinations or bias at the model level. What it does is change the operating relationship between the human and the agent so the human stays in the decision seat, their voice stays theirs, and the communities they serve stay visible. Who this is for: Anyone who works in or with communities, agilists, Organizational Designers, Systems thinkers, people who've read Wilber, and wants AI to serve those communities rather than extract from them. Also anyone who's noticed that their AI-assisted output is starting to sound like everyone else's. GitHub: https://github.com/seyekuyinu/integral-ai-commons My blog post about this: https://seyekuyinu.com/human-centered-ai-framework Happy to answer questions about the Integral Theory mapping, the agent behavior design, or the organizational layer. This is early so feedback welcome. submitted by /u/seyekuyinu [link] [comments]

I built an open-source token proxy that pseudonymizes PII without breaking LLM context

I've been working on an AI agent using Claude Opus to write KQL queries and triage security alerts. I don’t want to sen raw corporate logs (client IPs, real usernames, internal hostnames) to a cloud API. But when I tried standard PII redaction, the LLM's reasoning completely broke down. I wanted to share the architectural hurdles I hit and share the open-source proxy I built to solve it. The Problem with Naive Masking: First, I tried basic regex to swap [user@company.com](mailto:user@company.com) with [User_Email_1]. Claude immediately pushed back. Because LLMs are next-token predictors, a query like where User == "[User_Email_1]" is a statistical anomaly. To "fix" its own syntax, Claude started hallucinating realistic names like "sarah.kowalski" and querying for her instead. Next, I tried structured fakes using spaCy NER (swapping for [fake@email.com](mailto:fake@email.com)). This fixed the syntax but destroyed the context. If a user logs in from an IP in the Netherlands and then Russia, masking both as random 198.51.x.x IPs meant the LLM could no longer detect "impossible travel." My Solution: Context-Preserving Pseudonymization I realized a token proxy can't just be a dumb eraser; it has to be a translator. It needs to strip the PII but keep the metadata. • ASN-aware IP replacement: Using the MaxMind GeoLite2 database, the proxy swaps an IP with another IP from the same subnet/ASN. A real Hetzner IP in Germany becomes a fake Hetzner IP in Germany. The LLM can still run whois or spot impossible travel without ever seeing the real data. • Internal vs. External routing: I categorized entities so the LLM knows an internal corporate domain is talking to an external one, which is vital for triage logic. • Tail-buffering for SSE Streaming: When Claude streams token-by-token, a pseudonym can split across chunks (e.g., domain-inter in one, nal.com in the next). I built a tail buffer that holds the last 80 characters of each chunk to ensure strings are correctly unmasked on the way back to the user. The Code I decided to open-source the proxy engine. It's built with an Anthropic adapter right now, but the pseudonymization core is provider-agnostic. • GitHub Repo: https://github.com/zolderio/token-proxy • Blog: https://www.atticsecurity.com/en/blog/why-llms-hate-fake-data-token-proxy/ submitted by /u/rikvduijn [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

Built a personal context layer so your AI agents truly know you

No matter how much we use AI agents, every new session starts with zero context about us. It doesn't know what we were working on yesterday, what we've been looking into, or what we even care about. We end up re-explaining ourselves every time, and honestly half the time we can't even describe the full picture because it's all over the place, e.g., browsing, old conversations, coding sessions, etc. So we built AIContext using Claude Code. It reads local data files from supported sources (browser SQLite databases, AI coding session logs, etc.), normalizes everything into a single flat SQLite table stored in ~/.aicontext/, and exposes a read-only SQL interface that AI agents can query as a subagent. Each source is a plugin, so adding new ones is straightforward. The installation scans for supported sources on your machine, asks consent on each one, ingests the data, and sets up an hourly background sync. It works out of the box with Claude Code and other AI coding agents. We've been using it ourselves for the past few days and the agent started picking up on patterns we never consciously noticed: connections between things we were researching weeks apart, habits we didn't know we had, blind spots we couldn't have seen on our own. There's something strangely moving about an AI understanding you better than you understand yourself. After setup, you can ask things like: Do thorough research on my history, and infer my MBTI What is the biggest miss of my daily life that I may not even be aware of? Check my history and suggest what I should do this weekend Recommend a book, video, or podcast for me https://preview.redd.it/11dr02g1jsug1.png?width=1021&format=png&auto=webp&s=693310b13cb4338b91d53fd41222f8d8b8b787d8 How Claude was involved: The entire project was built with Claude Code. Claude helped design the plugin architecture, wrote the ingestion pipeline, and iterated on the subagent interface. We reviewed and directed all decisions, but Claude Code did the heavy lifting on implementation. What it is NOT: Not cloud-based. Everything stays in ~/.aicontext/ on your machine. Not a screen recorder. It reads existing local data files already on your machine. Not locked to any single agent platform. This is still early but functional. We'd love for people to try it, tell us what breaks or what's missing, and we'd truly appreciate contributions if this interests you. GitHub: https://github.com/SophonMe/AIContext Happy to answer questions here. submitted by /u/Cold-Emu-864 [link] [comments]