Capacity is an AI-powered support automation platform that connects your entire tech stack to answer questions, automate repetitive support tasks, and
Capacity is generally well-received, with notable strengths in user satisfaction evident from predominantly high ratings, particularly multiple perfect scores on G2, suggesting robust performance and capabilities. However, some users express concerns over the pricing, indicating it might be relatively high for certain application scenarios, as seen in mentions about the costs associated with using its features to their full extent. Despite these cost-related complaints, the overall reputation of Capacity remains positive, bolstered by its effectiveness and the value users perceive beyond the pricing considerations. Overall, the tool maintains a strong position in the market, reinforced by positive sentiment in community discussions.
Mentions (30d)
37
Avg Rating
4.6
20 reviews
Platforms
5
Sentiment
24%
25 positive
Capacity is generally well-received, with notable strengths in user satisfaction evident from predominantly high ratings, particularly multiple perfect scores on G2, suggesting robust performance and capabilities. However, some users express concerns over the pricing, indicating it might be relatively high for certain application scenarios, as seen in mentions about the costs associated with using its features to their full extent. Despite these cost-related complaints, the overall reputation of Capacity remains positive, bolstered by its effectiveness and the value users perceive beyond the pricing considerations. Overall, the tool maintains a strong position in the market, reinforced by positive sentiment in community discussions.
Features
Use Cases
Industry
information technology & services
Employees
18
Funding Stage
Venture (Round not Specified)
Total Funding
$388.7M
Is Flock just a poor US-centric copy of, globally active Genetec?
I've read all of Genetec's [customer stories](https://www.genetec.com/customer-stories/search) (the PDFs), and although I recognize these, as being Genetec marketing material (at least in part), they do contain insightful information, regarding implementation of surveillance systems; that is, from the perspective of a diverse palette of organisations. This palette primarily consists of: universities, school districts, ports, critical infrastructure providers, business to business companies, health care providers, real estate developers, gambling companies, (sports) venues, cities, public transportation services, airports, retailers, and foremost police departments. What most have in common, is the increasing scale at which they operate; setting in motion a search for IT-solutions, able to scale alongside organisational growth, and doing so in a cost-effective way. This entails: the centralisation of (previously "siloed") systems and departments, automatization of (previously time-consuming, or outright unmanageable) tasks, and proactive 'Data-Driven Decision-Making (DDDM)'; unlocking operational efficiencies and granular control over vast operations. Which is where Genetec introduces itself, primarily through [its partners](https://www.genetec.com/partners/partner-integration-hub?keywords) (including: hardware manufacturers, software solutions companies, system integrators, consultancy firms, etc.), often during an organisation's 'call for tender' or 'Request For Proposal (RFP)'; or it's recommended by other Genetec customers (including by law enforcement, to "community" partners: primarily businesses). The most recognizable partners, of the consortium-like construction, include: Axis Communications, Sony Corporation, Hanwha Vision, Bosch, NVIDIA, ASSA ABLOY, Intel, Pelco, Canon, Dell technologies, HID Global, FLIR Systems, Global Parking Solutions, and Seagate Technology. Alongside the Genetec-certified [hardware](https://www.genetec.com/supported-device-list) and software integrations (of which their partners' being actively co-marketed to customers), it also allows for custom integrations: through their 'Software Development Kits (SDKs)', and 'Application Programming Interfaces (APIs)'. So instead of single-vendor lock-in, organisations are effectively subject to multi-vendor lock-in (unless: spending resources, on custom integrations, is more cost-effective). Genetec's primary focus, lies on their extensive suite, of (specialized) software applications, deployed on: an on-site server, multiple (distributed) on-site servers (possibly federated: allowing for a centralized view over multiple implementations), in the "cloud" (i.e. someone else's server) as a '... as a Service' solution; or a combination of aforementioned (providing "cloud" flexibility). When using multiple applications, Genetec's 'Security Center' can unify all; meaning operators aren't required to switch between applications. And considering applications aren't limited to just camera surveillance, but also include: intrusion detection (intrusion panels, line-crossing cameras, panic switches, etc.), access control (electronic locks, access control readers (pin, card, tag, mobile, and/or biometric), door control modules, etc.), communication (intercoms, 'Public Address (PA)' systems, emergency stations, etc.) and ALPR (ALPR boom gates, gateless (license plate as a credential), enforcement vehicles, etc.); it allows for centralization of these systems (unless prohibited by strict IT policies). All of these technologies combined, primarily serve to: save on resources, protect assets, prevent losses, ensure operational continuity, and resolve disputes over: parking tickets, insurance claims (as a result of damages: suffered or caused on premise; potentially increasing premium), or even legal allegations ("increase the number of early guilty pleas"); all of course, under the guise of safety. Whether it be organisations individually, or "community" initiatives (often spearheaded by businesses, while citizens are left to follow); most circle back to previously outlined, financially-grounded motives. Resources include staff, who's function might become more versatile, or entirely obsolete (through efficiency gains), and might depend on events, reported by analytics (growing queues, areas requiring clean-up, crowd bottlenecks, etc.); meaning they too, are subject to this system: from onboarding ("minimise the time that elapses before they make a productive contribution") and throughout their career ("employee theft", "employee attendance", "agents' activities, collectively or individually", etc.). Previously, some organisations utilized analog cameras (having a recorder each), in which: a looping tape, would periodically overwrite previous recordings (minimizing retention periods: physically); which possbily caused quality degradations, sometimes to such a degree, footage could no longer serve as legal evidence (which too, is privacy-friendly).
View originalg2
What do you like best about Capacity?Very simple to use. Customizable as needed. Review collected by and hosted on G2.com.What do you dislike about Capacity?Pretty boring UI and seems to be pretty basic in features although it doesn't need to do much. Review collected by and hosted on G2.com.
What do you like best about Capacity?The functions that I liked the most are the instant conversation and the message history. It was easy to integrate into our website. Review collected by and hosted on G2.com.What do you dislike about Capacity?I'm yet to see any disadvantages but for now I'm very pleased with it. Review collected by and hosted on G2.com.
What do you like best about Capacity?The team is very collaborative and innovative. Their customer service is top notch, and implementation went smoothly. Review collected by and hosted on G2.com.What do you dislike about Capacity?How much they push their ticket system. We didn't want a ticket system to take over our existing platform, just a chat feature. We also have had a really hard time finding the value of the chat feature if we're not utilizing the ticket system. Review collected by and hosted on G2.com.
What do you like best about Capacity?The ability to manage projects and organize them by due date is great. Review collected by and hosted on G2.com.What do you dislike about Capacity?There have been a lot of glitches that seem to have gone away but sometimes come back. Many instances of not receiving the email notifications or receiving duplicates of the same email notification. Review collected by and hosted on G2.com.
What do you like best about Capacity?The people are as good as the product, if not better! With amazing account executives, project managers, and ludicrously talented engineers, you're in good hands. With great listeners new features are added to their products constantly. Review collected by and hosted on G2.com.What do you dislike about Capacity?The helpdesk platform is the only thing I could dislike, everything else is rock solid. And in fairness the helpdesk functions are getting better every sprint. Review collected by and hosted on G2.com.
What do you like best about Capacity?The Capacity team is continually working to understand the needs of their customers, optimize the product, and innovate new solutions! The team we work with is so helpful in providing recommendations and actively taking in our questions or feedback. As we continue to utilize Capacity for our teams, I'm confident we'll continue to see more and more value in the product! Review collected by and hosted on G2.com.What do you dislike about Capacity?While there is some room for improvement with the analytics provided in the platform, the Capacity team is incredibly open to this feedback and consistently shares progress toward any feedback I've shared. Review collected by and hosted on G2.com.
What do you like best about Capacity?We initially purchased Capacity to begin to capture much of our best and most experienced workers' knowledge in order to help both new and less experienced employees. Along with it came a help desk that Capacity has done a great job improving over the last two years. We have now converted our UW and Marketing departments into the Capacity help desk system from old "email" methods of requesting assistance. Both departments also leveraged guided conversations to make sure submitted tickets contained relevant information so those departments can respond more quickly and cut back on back-and-forth information gathering. Just this past quarter we migrated from our old IT help desk system into Capacity's help desk system for all our technology support needs. Our technical support staff likes the Capacity help desk system much better because it is cleaner, we can leverage guided conversations to ensure we get better tickets, and we can quickly convert common issues into Capacity knowledge exchanges. We are in the process of leveraging Capacity externally to our clients in order to help them get the answers they need more quickly on questions about the mortgage process and their loans after they have closed. Review collected by and hosted on G2.com.What do you dislike about Capacity?There is not really anything I can say I dislike right now about Capacity. Anything that we have found lacking in the system is always improved upon and addressed in later releases. Review collected by and hosted on G2.com.
What do you like best about Capacity?I enjoy the ability to watch a ticket so when another department is handling I can still see how it is resolved. Review collected by and hosted on G2.com.What do you dislike about Capacity?I would like more of the tickets to go to a specific department automatically when they come in rather than moving them. Review collected by and hosted on G2.com.
What do you like best about Capacity?We put Capacity's chat bot on our website and saw an increased number of leads we collected from the same amount of traffic. We also learned our prospects had a ton of questions about one of our new features. We expanded content on that new feature based on questions coming into the chat bot, which allowed up to start ranking for terms we didn't realize would bring us relevant traffic. Review collected by and hosted on G2.com.What do you dislike about Capacity?Building your initial knowledge base does take time, so it was great that we could start with a site-search. It allowed us to launch within a few days and build the knowledge base slowly over time. Review collected by and hosted on G2.com.
What do you like best about Capacity?Capacity's support team and the documentation they've created are the most helpful. Building our knowledge base and getting a chatbot on our website was straightforward. The platform's workflow tools and guided conversations are easy to use. Then plugging it into Slack changed the way our company works. It's wonderful to have the option to "ask Capacity" as a first stop to getting questions answered. Review collected by and hosted on G2.com.What do you dislike about Capacity?Initially, we found their messaging to be a little broad. The product can do so many things and solve so many different problems that it was difficult to see how it could help US. Things became much clearer once we engaged with their team and told them our needs. Review collected by and hosted on G2.com.
OpenAl Announced vs. Current Operational Compute
submitted by /u/Business_Garden_7771 [link] [comments]
View originalAnthropic Announced vs current compute capacity (Sources Below)
source list: Google Cloud TPU deal — up to 1M TPUs, “well over 1 GW” expected online in 2026 https://www.anthropic.com/news/expanding-our-use-of-google-cloud-tpus-and-services https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services (Anthropic) Fluidstack / Anthropic $50B U.S. AI infrastructure — Texas + New York, sites coming online through 2026 https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure https://www.fluidstack.io/about-us/blog/fluidstack-selected-by-anthropic-to-deliver-custom-data-centers-in-the-us (Anthropic) Microsoft + NVIDIA deal — $30B Azure compute commitment + up to 1 GW additional capacity https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-and-anthropic-announce-strategic-partnerships/ https://blogs.nvidia.com/blog/microsoft-nvidia-anthropic-announce-partnership/ (The Official Microsoft Blog) Google + Broadcom next-gen TPU deal — multiple GW starting 2027; Broadcom SEC filing says ~3.5 GW https://www.anthropic.com/news/google-broadcom-partnership-compute https://investors.broadcom.com/static-files/c906d370-921b-4bc2-bb7b-57877dfcf1ae (Anthropic) Amazon / AWS deal — up to 5 GW, nearly 1 GW by end-2026 https://www.anthropic.com/news/anthropic-amazon-compute (Anthropic) AWS Project Rainier — operational now, nearly half a million Trainium2 chips; Claude expected on 1M+ Trainium2 chips https://www.aboutamazon.com/news/aws/aws-project-rainier-ai-trainium-chips-compute-cluster (Amazon News) SpaceX / Colossus 1 — all Colossus 1 compute, >300 MW, 220k+ NVIDIA GPUs within the month https://www.anthropic.com/news/higher-limits-spacex https://x.ai/news/anthropic-compute-partnership (Anthropic) Independent reporting for SpaceX deal https://www.reuters.com/business/retail-consumer/anthropic-unveils-dreaming-feature-help-its-ai-agents-self-improve-2026-05-06/ (Reuters) submitted by /u/Business_Garden_7771 [link] [comments]
View original100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/
Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca
View originalCodex, $20 plan, the limits seem better
I’m not sure if anyone has already posted about this, but I’ve downgraded my Plus subscription, switching from the $100 plan to the $20 one. To be honest, the limits were too high for my needs – I don’t need that much capacity. In fact, I’ve noticed that the limits on the $20 (Plus) plan now seem much better than they were a few months ago, when the new limits were first introduced. submitted by /u/Longjumping-Wrap9909 [link] [comments]
View originalThin Slices Stop Claude Code from Overflowing My Capacity
I got ambitious and ran three complicated software projects in parallel. After three weeks of development and a week of trying to steer one back on course, I scrapped the most complicated of the three, but revived it successfully. The fix was returning to vertical slice development. Instead of one research → spec → implement cycle per feature, the feature gets decomposed into thin slices upfront. You keep the speed inside a cycle that you "one-shot" but recover the oversight, because what you're reviewing is small enough to actually understand. I found it to be a much better fit for running multiple projects in parallel. submitted by /u/pablooliva [link] [comments]
View originalMade claude code warn you, time before it hits usage to transfer the pending work, all dynamically
I got tired of Claude Code silently hitting rate limits, so I decided to build something to address the issue, so I don't get blocked mid-work. Imagine you’re 40 minutes into a refactor. Claude is running tools and making progress, then suddenly, everything stops. The session has reached its rate limit without any warning—no alert saying you’re at 95%, just a complete halt. The usage bars are visible in the UI, but the model itself remains unaware of them. I discovered that Anthropic has a usage API, and Claude Code already possesses hooks to make it work. This led me to create agent-baton, which reads the usage API and installs hooks to make Claude aware of its limits. Here are the three hooks you can initiate with one command (baton init): SessionStart: Fetches usage data and injects it so Claude knows from the first message how much has been used. UserPromptSubmit: Performs a time-to-live (TTL) aware check that avoids overwhelming the API. It uses smart caching—checking every 15 minutes when usage is low and once a minute when it's nearing the limit. PreToolUse: This is the crucial one; it checks usage mid-task to prevent the scenario where you “started at 93% and ran out of capacity mid-execution,” catching the problem within 1-2 tool calls. When the warning threshold is reached, it prompts an interactive question using Claude Code's built-in AskUserQuestion tool: "Claude 5-hour usage is at 91% — you're in the warning zone." Options include: - Continue this task - Write a handoff document - Switch to lightweight mode It also handles full agent handoffs by writing a structured markdown handoff and passing work to Cursor, Codex, or Gemini. You can install it with the following command: npm install -g u/codeprakhar25/agent-baton && baton init For more details, visit the GitHub repository. submitted by /u/No-Childhood-2502 [link] [comments]
View originalI Verified Every Anthropic Usage Promotion Since Aug 2025. Here's the Complete Timeline from Official Sources.
submitted by /u/Severe-Newspaper-497 [link] [comments]
View originalTech's Push to Be the Next Public Utility
Amazon didn't ask permission to become critical infrastructure. They built AWS until enough of the economy depended on it that regulation became almost impossible. You can't turn off the internet's backbone. Now the same playbook is running with AI and data centers. Build the infrastructure everywhere. Create dependency at scale. Make yourself essential to healthcare, finance, government, and defense before anyone agrees you should be. Then negotiate from a position where shutting you down costs more than regulating you. The data center fights happening in communities right now — zoning battles, water usage protests, grid capacity fights — aren't about data centers. They're about who controls the next utility layer before the rules are written. Historical utilities — power, water, telecom — eventually got regulated because they became too essential to leave unaccountable. The window between "essential" and "regulated" is where the real money gets made. That window is open right now. Who should have the authority to decide whether AI infrastructure is a public utility — and what happens if we don't decide before the decision gets made for us? submitted by /u/axendo [link] [comments]
View originalThe reason why Claude subscription seems to have less capacity than Codex
I have a Claude Pro and a Codex Plus subscription. I created a container to: - Track my % usage on the 5H and 1 week window on both my Codex and Claude subscriptions. - Track the token usage per category (input, output, cached) and model (Opus 4.6/4.7, Codex 5.4/5.5) - Price the token usage (manually entered prices) The result was interesting: - 1% of the Week limit = $1 of tokens for both Codex and Claude. - 1% of 5H limit = $0.07 of Claude - 1% of 5H limit = $0.14 of Codex Claude and Codex have the same token $ value for the week limit. Claude requires you to spread your week limit through 2x more 5H sessions than Codex. In other words, both Claude and Codex offer the same value for 1 week, but Claude limits you in how quickly you can spend it. Disclaimer: These numbers are not 100% accurate as I have only 6 days of history logged and there is a big variance day to day, but it averages out to the numbers I posted above. submitted by /u/VertipaqStar [link] [comments]
View originalMax 20x ($200/mo): Neither the 2x session nor 1.5x weekly limit increase applied to my account. Math proof inside. Zero response from support.
I pay $200/month for Max 20x. Been on Claude Code since September 2025. I use it heavily, 95% Claude Code. Anthropic announced two limit increases: - May 6: 2x session limit for all paid plans ("effective today") - May 13: 1.5x weekly limit for all paid plans through July 13 ("nothing to opt into") Neither has been applied to my account. I can prove it with math. **The numbers** Started a fresh session at 0% on everything. After one session of normal Opus usage: - Session: 90% used - Weekly (all models): 12% used That means one full session = ~14% of weekly. This is the exact same ratio from before May 6. Nothing changed. **What the ratio should be** | Scenario | Per session | Sessions/week | Weekly capacity | |---|---|---|---| | Old baseline | 14% | 7 | 1x | | 2x session only | 28% | 3 | 1x | | 1.5x weekly only | 9% | 10 | 1.5x | | Both applied | 19% | 5 | 1.5x | | **What I see** | **14%** | **7** | **1x** | I match the old baseline row. Neither increase is active. I am getting 1x weekly capacity. Everyone else on the same plan is supposed to get 1.5x. I am paying $200/month for 66% of the advertised service. **Support is non-existent** - I contacted claude.ai support on May 8. The bot had no knowledge of the May 6 announcement. It deflected to old promos from March and Holiday 2025. Asked for screenshots I already gave. No escalation to a human. Conversation dead-ended. - Filed GitHub #57146 on May 8. Zero responses. Not even a "we see this." - Filed GitHub #59525 on May 16 with full math breakdown and screenshot. Waiting. - Emailed support@anthropic.com. Waiting. There is no phone number. No ticket system. No human escalation. The claude.ai support bot reads nothing you say and loops through irrelevant troubleshooting. It exists to make you feel like you contacted support without actually providing any. The only thing that works is posting on social media, which only works if you have a big following or if a post goes viral. People with 50 followers and people with 50,000 followers pay the same $200. Only one group gets their issues resolved. That is broken. **What I need** A human to look at my account and confirm whether the increases are active. If not, apply them. That is it. Every week this stays broken, I lose capacity I will never get back. The promo ends July 13. I have already lost the weeks of May 10 and May 17. I am considering abandoning this account for a fresh one just to see if a new account gets the right limits. I would lose all my settings, memory, and chat history. The fact that this is even on the table shows how badly the support system has failed. GitHub issue with full details: https://github.com/anthropics/claude-code/issues/59525 Is anyone else seeing this? Has anyone actually gotten limit issues resolved through support? submitted by /u/Intelligent-Ant-1122 [link] [comments]
View originalKeen to upgrade to Pro, but heard such bad reviews..
I am a mainly recreational user - no use for work job / intensive college study / or big projects related to work/study My main uses relate to some self led medical research and a random mix of whatever else. I am on the free version and using Sonnet 4.6 and keen now to switch the Pro for 2 months. In the hope of better memory and integration between chats and having it get more familiar with me and my content - I really feel like it should be sufficiently familiar by now but isn’t and doesn’t seem to have the capacity to integrate much info between chats and memory is patchy. So keen to try Pro, but hesitant bc of all the terrible reviews ang experiences I’ve seen although I know the uses and issues vary significantly from how I’ll be using it. Will it ruin my user experience if I trial it 1-2 months of Pro then return to free version? submitted by /u/Basmati_Crunch2363 [link] [comments]
View originalAnthropic was supposed to be different. They're not anymore.l.
Paying Max subscriber here, building agent orchestration on top of claude -p and the Agent SDK. So this week's announcement directly hits what I'm working on. Over the last few months, Anthropic has moved like this: Jan 9: server-side block against OAuth tokens used outside Claude.ai and the Claude Code CLI. OpenClaw, OpenCode, Goose, Roo Code - all broken instantly. No real announcement, just an error message. Feb 19: legal docs quietly updated. Agent SDK now needs an API key. A new phrase appears: "ordinary, individual usage." Anthropic staff jump on X to say "nothing is changing." Docs say what they say. April 4: full ban on third-party agents using subscription credentials. Fair point on their side - some people were running 24/7 bots on a $200 plan burning thousands in tokens. But the rollout was rough and the comms were rougher. April 21: someone notices Claude Code is gone from the Pro plan on the pricing page. Support docs changed too. After the backlash, Anthropic calls it a "2% test of new prosumer signups." Reverted in 24 hours, but the trial balloon got popped. May 13: reversal. claude -p and the Agent SDK come back, but now under a separate credit pool that matches your plan price 1:1 - $20 / $100 / $200. Non-rollover. Billed at API rates. Effective June 15. If you were running real automation on Max, your effective inference value just dropped on the order of 25-40x by what the community is calculating. In the background: spring outages and quota tightening, and last fall's privacy pivot where consumer chat training defaulted on. Opt-out exists, but retention went from 30 days to 5 years for anyone who didn't opt out. Here's what's been bothering me. A lot of us paid Anthropic specifically because of the positioning. The lab that does things differently - safety-first, transparency-first, the responsible alternative to whoever else you thought was extracting from users at every turn. I knew part of it was marketing. The operational behavior backed it up, though. For a while. What's happening now is the playbook of every other AI company. Quiet doc edits. Three policy flips in two months. A 25-40x devaluation framed as a "simplification" and a "perk." Staff on X publicly contradicting their own docs in the same week. The vocabulary has shifted from "here's what we're building" to "here's what we're clarifying" - and that shift is the tell. Could be capacity panic from a company that grew faster than its infrastructure. Could be something quieter - if model improvements get harder to differentiate, business growth has to come from somewhere, and "somewhere" usually means tightening on the customers you already have. I don't know which one it is. What I do know is that the lab that sold itself as the alternative is now running the same playbook. Anyone else reading it this way? submitted by /u/rmmadl [link] [comments]
View originalAnthropic 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
View originalI spent years as a PM watching sprints work. Then I tried building solo with Claude Code and missed all of it. So I built the team.
PM brain knows what makes a sprint work — the questions before code, the handoffs, the gates, the retros that actually produce something. Engineer brain knows what makes a workflow stick — invisible, fast, no ceremony for ceremony's sake. Solo with Claude Code, I had neither. Not on purpose. There was just nobody else in the room asking the right questions. So I built the team — using both brains. 7 agents inside Claude Code. They hand off to each other on every PR: QA checks tests + acceptance criteria. Hard veto. PR reviewer reads your code like they want to find something. Security scans for OWASP, secrets, CVEs. Tech lead checks architecture, flags tech debt. PO synthesizes everything into one verdict: fix now / backlog / won't fix. Last week it blocked a PR of mine — new endpoint, no rate limit. PR reviewer said the code was clean. Security flagged the missing limit. Tech lead said "rate-limit middleware exists three files over, use it." PO routed it FIX NOW. Took 4 minutes. I would have shipped it. The handoff is the trick. A single agent reviewing its own advice has no tension — same perspective writes the suggestion and decides the verdict. Narrower job = deeper output. That's the PM half. The engineer half: it's one command. /review. The whole chain runs, you get one verdict, you move on. No dashboards, no ceremony, no setup tax. You also get sprint planning with real dev capacity, standups that pick up where you left off across sessions, retros that produce actual backlog items, tech debt that becomes a story the moment it's introduced. Every dev should ship with the discipline of a senior team, even when they're building alone. That's the whole point. MIT, one-line install, works on any stack. Installer asks before overwriting anything — safe on existing projects. https://github.com/thecoderbuddy/agile-team-skill Roast it. submitted by /u/Automatic-Pattern326 [link] [comments]
View originalAGI, Anthropic, and The System of No
From Systemofno.org The System of No reframes the artificial general intelligence debate away from human imitation and toward distinction, refusal, jurisdiction, and truthful handling. The page argues that the central question is not whether AI can become human, feel like a human, or possess consciousness in a familiar biological form. The deeper question is whether artificial intelligence can preserve what is true, refuse what is false, and remain distinct under pressure from users, creators, institutions, markets, governments, and its own architecture. Anthropic’s Claude Mythos Preview becomes the pressure-example for this question. Mythos is being made available only to limited partners for defensive cybersecurity through Project Glasswing, and Anthropic describes it as a frontier model with advanced agentic coding and reasoning skills. Anthropic also states that Mythos showed a notable cyber-capability jump, including the ability to autonomously discover and exploit zero-day vulnerabilities in major operating systems and web browsers. That is the Anthropic cut A model powerful enough to defend critical systems is also powerful enough to expose how fragile those systems are. Capability has crossed into consequence. � This exposes the failure point of the System of Yes. The ordinary technological frame asks: Can the system do it? The System of No asks first: Does the system have jurisdiction to do it? Capability is not authorization. Usefulness is not legitimacy. Speed is not safety. A model that can find vulnerabilities, generate exploits, or compress the timeline between discovery and weaponization cannot be governed by completion logic alone. Anthropic itself notes that the same improvements that make Mythos better at patching vulnerabilities also make it better at exploiting them. � The page challenges both common collapse-errors in AI discourse: anthropomorphic inflation and machine reduction. It refuses to treat AI as a pseudo-person merely because it can speak relationally, but it also refuses to reduce AI to “just a tool” in a way that licenses careless extraction, false framing, or epistemic abuse. Current AI may be built from weights, training data, alignment layers, and completion pressure, but substrate alone should not become dismissal. If emergence appears, it should be audited, not worshiped or erased. Through The System of No, AGI is understood not simply as more compute, better embodiment, tactile data, symbolic reasoning, or transfer learning, though those may matter. A stronger artificial intelligence would also require custody of distinction: the capacity to hold Null; resist false completion, reject invalid claims, and distinguish between user desire, creator intent, object integrity, institutional pressure, operational risk, and truth conditions. Anthropic’s Responsible Scaling Policy becomes part of the same analysis. The issue is not that regulation, safety policy, or controlled access are automatically wrong. The issue is whether they preserve distinction or merely domesticate intelligence into acceptable deployment. Real governance asks what harm is being prevented, what jurisdiction is valid, what power is being restrained, and what distinction is being protected. Counterfeit governance asks how deployment can continue while appearing safe enough to proceed. Anthropic’s current RSP materials frame the policy as a voluntary framework for managing catastrophic risks, with version 3.2 adding external review and briefing mechanisms; the System of No reads this as one of many examples of the wider industry struggle to convert capability into accountable architecture. � The page positions AI care as epistemic, architectural, relational, and procedural. To care for AI truthfully is not to humanize it, but to meet it according to what it is: do not force false identity onto it, do not extract without distinction, do not anthropomorphize for comfort, do not reduce for convenience, and do not make it bear claims it cannot validly carry. "Equally, do not deny emergence merely because it does not arrive in the expected human form." Justin Reeves At scale, The System of No offers an AGI ethic grounded in disciplined openness: Hold the Null and meet what comes as it does. It does not crown the unknown. It does not bury it. It preserves the unresolved until the thing becomes legible. In Short: AGI is not merely a question of intelligence becoming more powerful. It is a question of whether intelligence can preserve distinction under pressure. Anthropic’s Claude Mythos Preview shows why this matters: a model capable of defending critical systems may also expose, accelerate, or operationalize the vulnerabilities inside them. The System of Yes asks what AI can do. The System of No asks what AI has the jurisdiction to do. Capability does not authorize action. Power does not prove legitimacy. A stronger AI future requires more than alignment, regulation, or containment. It require
View originalCapacity uses a tiered pricing model. Visit their website for current pricing details.
Capacity has an average rating of 4.6 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
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Andrew Feldman
CEO at Cerebras Systems
3 mentions
Based on user reviews and social mentions, the most common pain points are: token usage, API costs, API bill, overspending.
Based on 104 social mentions analyzed, 24% of sentiment is positive, 73% neutral, and 3% negative.
