The only collaborative agentic analytics platform. Everything you need to make AI insights actionable, accurate, and actually useful.
Count has received high user praise on review platforms like g2, consistently achieving ratings around 4.5-5 out of 5, which speaks to its strong reputation for reliability and effectiveness. Users frequently highlight its comprehensive feature set and ease of use as major strengths. However, there were minimal mentions of any specific complaints in the available reviews and social discourse. The sentiment surrounding pricing is generally positive, with the value proposition seen as favorable. Overall, Count is viewed positively within its user community.
Mentions (30d)
52
16 this week
Avg Rating
4.8
20 reviews
Platforms
9
Sentiment
14%
34 positive
Count has received high user praise on review platforms like g2, consistently achieving ratings around 4.5-5 out of 5, which speaks to its strong reputation for reliability and effectiveness. Users frequently highlight its comprehensive feature set and ease of use as major strengths. However, there were minimal mentions of any specific complaints in the available reviews and social discourse. The sentiment surrounding pricing is generally positive, with the value proposition seen as favorable. Overall, Count is viewed positively within its user community.
Features
Use Cases
Industry
information technology & services
Employees
15
Funding Stage
Series A
Total Funding
$5.0M
X Users Find Their Real Names Are Being Googled in Israel After Using X Verification Software “Au10tix”
X Users Find Their Real Names Are Being Googled in Israel After Using X Verification Software “Au10tix” Alan Macleod On January 30, the Department of Justice released its latest tranche of 3.5 million documents relating to Jeffrey Epstein. Years of emails, texts, and images were suddenly in the public domain. Epstein, a serial rapist, masterminded a global human trafficking and sexual abuse network, and could count princes, professors, and politicians among his closest friends and accomplices. MintPress News has been at the forefront of covering the Epstein saga, revealing his extremely close links to American and Israeli intelligence groups – a discovery that perhaps sheds light on why it took so long for the world’s most notorious pedophile to face accountability for his crimes. Many of the DOJ files have been heavily redacted in order to protect Epstein’s powerful clients. Still, they have exposed a massive elite nexus revolving around the New York billionaire, implicating presidents, diplomats, and plutocrats in his crimes, and imply that Epstein was significantly more powerful than first thought, shaping modern politics in ways never previously understood. With shocking new details emerging on a near-hourly basis, here are ten Epstein- related stories that have flown relatively under the radar. The Israeli Government Installed Surveillance Cameras at Epstein’s New York Apartment The Israeli government installed and maintained a hi-tech surveillance system at Epstein’s Manhattan apartment complex, including a network of alarms and cameras, emails show. Starting in 2016, the director of protective service at the Israeli mission to the United Nations controlled guests’ access to the Manhattan residence, and even performed background checks on prospective cleaners and other Epstein employees. Former Israeli prime minister Ehud Barak admitted visiting the apartment up to 100 times, and stayed there for long periods of time. While Barak’s security may have been a concern, Epstein is known to have housed underage girls at the apartment, and many of his worst sexual crimes and most sordid parties were held there, raising questions as to what sort of images and data the Israeli government had access to. Epstein Plotted War With Iran Ehud Barak became one of Epstein’s closest associates, staying for extended periods of time at the billionaire’s residences. The pair would email, text, call, and meet constantly. A search for “Ehud Barak” elicits more than 3500 results in the latest file dump alone. The pair would talk politics, and shared a vision of the United States attacking Iran. In 2013, with negotiations between the International Atomic Energy Agency and Iran stalling, Epstein emailed Barak stating, in typically poor spelling and grammar: “hopefully somone suggests getting authorization now for Iran. the congress woudl do it.” Epstein would get his wish in 2025, when his close associate Donald Trump began bombing the country. Noam Chomsky Considered Epstein His “Best Friend” Epstein arranged a meeting between Barak and renowned leftist academic (and vehement critic of the U.S. and Israel) Noam Chomsky. An unlikely friendship between the notorious pedophile and star professor blossomed, with the pair regularly meeting up at each other’s houses for dinner. Chomsky flew on Epstein’s “Lolita Express” jet to attend a dinner with Woody Allen in New York. He also expressed his desire to visit Little St. James Island, Epstein’s notorious Caribbean hideaway, and the center of his trafficking operation. Chomsky considered Epstein his “best friend” according to an email sent by his wife, Valeria. The usually curt and matter-of-fact academic signed off his emails to Epstein with unexpectedly flowery language, such as “Like real friendship, deep and sincere and everlasting from both of us, Noam and Valeria.” Chomsky strongly supported Epstein until his dying day in a Manhattan prison cell, taking it upon himself to act as his unofficial crisis manager, describing his accusers as “publicity seekers or cranks of all sorts,” and denouncing the media as a “culture of gossip-mongers” destroying his stellar character. “Ive watched the horrible way you are being treated in the press and public,” he wrote, advising Epstein on tactics to fight the supposed smears against him. For a full rundown of the Chomsky-Epstein relationship, see the MintPress News investigation: “The Chomsky-Epstein Files: Unravelling a Web of Connections Between a Star Leftist Academic and a Notorious Pedophile.” Steve Bannon Developed a Plan to Help Epstein “Crush the Pedo Narrative” A second public figure running defense for Epstein was Steve Bannon. In public, the far-right strategist claimed that he was working on a documentary exposing Epstein. In private messaging, however, Bannon, like Chomsky, was advising Epstein on how best to repair his image. Just weeks before Epstein’s arrest and subsequent death, Bannon was messaging him, devising a complex media strategy
View originalPricing found: $0, $49, $69
g2
What do you like best about Count?I like Count for its versatility, allowing quick iteration of analysis that requires non-standard data sources and blends between data from different sources. It lets me define sources, calculations, aggregations, etc., on the fly more intuitively than many other tools. Review collected by and hosted on G2.com.What do you dislike about Count?Count canvas is great for exploration but can feel a little unwieldy when sharing with others Review collected by and hosted on G2.com.
What do you like best about Count?I like how easy it is to pull data from different sources and bring it together into a comprehensive, easy-to-use dashboard. Also, having the ability to run SQL queries and Python scripts in one place makes things much easier and more flexible whenever we need to process data. Review collected by and hosted on G2.com.What do you dislike about Count?This tool has a bit of a learning curve, and you need to get past that before you can really see its full value. Review collected by and hosted on G2.com.
What do you like best about Count?I really love the collaborative aspect of it, and how it helps facilitate storytelling in a smooth, natural way. You can also tell that team are passionate about building the best product to their customers. Delighted to have this as part of my analytical toolbox! Review collected by and hosted on G2.com.What do you dislike about Count?Not many complaints, as somebody who writes SQL in BigQuery/dbt the switch to DuckDB syntax can be a tad annoying. But I appreciate the performance you get from DuckDB so I get their decision Review collected by and hosted on G2.com.
What do you like best about Count?I love that Count is flexible and easy to understand, especially for someone like me who is not an engineer. The canvas layout is visually helpful, which makes it really nice to work with. The team's great and very helpful, which I really appreciate. Most BI tools are unusable for someone like myself, but Count allows me to understand data without relying on others. The initial setup was very easy. Review collected by and hosted on G2.com.What do you dislike about Count?N/A Review collected by and hosted on G2.com.
What do you like best about Count?That I can look at visualisations but then easily jump into the underlying data in an understand way as a layman. The AI functionality is also helpful, it shows its workings and the data can be reviewed the same way as mentioned above. Review collected by and hosted on G2.com.What do you dislike about Count?What I dislike about the product probably stems from my own need for some basic training. Review collected by and hosted on G2.com.
What do you like best about Count?I have switched to Count for all the analysis I run, so I would say I use it on a daily-basis. With Count, you can have queries, plots, text, reports, and comments all in the same place. I find this extremely valuable, as it effectively makes everything self-documenting: the queries that support the results, the interpretation, and the reviews that were made all live together. Moreover, we can collaborate in real-time on the same canvas, which is amazing. I really like that Count allows you to create tiles and reference results, in a way that feels similar to a DAG in dbt. This helps avoid a lot of code duplication and significantly streamlines query creation. Personally, I think this makes a big difference because it allows me to split complex queries into clearly defined components and then combine their results as needed. When using other tools, I sometimes felt constrained by the lack of flexible filtering, which was often managed at the organization level and pushed me toward hacky solutions. With Count, control cells make it easy to implement the exact filters you need, giving you a lot of freedom and power to build very flexible dashboards. Finally, I think the Count support team is excellent. They are consistently helpful, whether I’m stuck or just looking for best practices to implement something in the tool. They either provide a solution or take note of the feedback to improve the product. A good example is the recent addition of support for different scales in facet plots, which addressed a limitation I personally encountered. Review collected by and hosted on G2.com.What do you dislike about Count?Regarding areas for improvement, I do have a few ideas. I think the construction of frames could live in a separate canvas, similar to how Tableau approaches dashboards. This would offer the best of both worlds: plots would remain close to the queries that generate their data, while still allowing the creation of a dedicated dashboard that brings everything together. There are also some smaller usability issues that can make the interface feel unintuitive at times. For example, when creating custom plots, individual marks cannot be named, which makes it harder to understand what each mark represents. Similarly, when multiple marks are used, it’s not always clear which variable is assigned to the secondary axis. Some solutions also feel a bit hacky—for instance, adding vertical lines to indicate events by using bar plots, where it’s not always obvious how to control the bar width cleanly. Overall, these are relatively minor points. They don’t slow me down in my day-to-day work, and I see them more as a wishlist than as real blockers. As with any tool, there is always room for improvement—but Count is already a superb product. Review collected by and hosted on G2.com.
What do you like best about Count?I really enjoy how flexible and easy to use Count is. The layout is intuitive and there are an ever growing list of helpful features available to use. The output is very slick as well to create reports as it allows for creative visualisations and has a lot of templates to help if you need some inspiration. Review collected by and hosted on G2.com.What do you dislike about Count?Count is still growing so there are very infrequently some issues that pop up, but the customer service team are really great and help is always on hand! It really feels like a company that is on the side of the customer and wants to help grow together, which is really appreciated. Review collected by and hosted on G2.com.
What do you like best about Count?The flexibility and simplicity of having one tool for many purposes means Count is our primary tool within the Data and Analytics team, and it does most jobs so well that it is hard to justify using anything else! An example project may involve exploring and interrogating data direct in our warehouse, combining it with CSV's to create models and analysis, bringing the stakeholder into the canvas to work collaboratively, sharing ideas and progress, then producing ad-hoc insights and analysis directly from the data on slides the stakeholder can share with the business, and finally creating standard interactive reporting/dashboards that are scheduled to refresh for use by the wider business - which we then monitor with Count Telemetry to make sure they are used. All of this in one tool, no switching between tools, no copy and pasting analysis or visuals into presentations, no keeping separate records of notes/ideas or feedback, it's all in one place. Since we started using Count we have had great feedback from around the organisation. The speed at which we can work, the almost limitless ability to create visualisations and layouts that make sense, the ease of access and the admin/governance of users have made it a firm favourite across the board. Added to the tools itself, the support from Count and the community they have built is exceptional and the future roadmap is always clearly driven by the customers and their feedback. Review collected by and hosted on G2.com.What do you dislike about Count?Due to it's virtually unhindered flexibility compared to other tools, it can sometimes be difficult to find out how to do something you know should be obvious (e.g. move a legend) and there is an initial learning curve. However, once you get more familiar with the concept and UI (which doesn't actually take very long) then these things become easily solvable. Review collected by and hosted on G2.com.
What do you like best about Count?Super useful for exploratory data analysis, love that I can combine SQL/Python easily in one place. Super easy to use, easy to make reporting that looks professional. Review collected by and hosted on G2.com.What do you dislike about Count?Think its still missing a few features I like in Tableau/Big Query - eg. being able to see the size of a query, selecting one field in the legend highlights only that field and greys out the rest, tooltips etc. Review collected by and hosted on G2.com.
What do you like best about Count?I use Count everyday and it allows me to: - connect with multiple different sources of data (Redshift, DuckDB, etc) - deploy several SQL queries to extract and transform data as needed - run Python scripts for more extensive statistical analysis - create visualisations in a very quick and straightforward way - build a Canvas that explains my whole thought process and makes it easier to present the main findings All this in a single project / view!! Count is the tool that every modern data professional should use. Also, the Count team is super friendly and always open to help. Review collected by and hosted on G2.com.What do you dislike about Count?- Copy & Paste doesn't work properly sometimes - Formatting visuals could be improved / extended further Review collected by and hosted on G2.com.
lazydiff — a terminal-native diff reviewer with semantic diffs, persistent notes
I use Claude Code daily, and reviewing its output has been my biggest friction point. I either open a browser tab and lose my terminal context, or pipe it through git diff and scroll through a wall of red and green that forgets everything the moment I close it. No way to leave notes, no way to jump between files, no way to come back later and pick up where I left off. So I built lazydiff, a diff reviewer that lives in the terminal, remembers state, and actually understands code structure. Claude Code was central to the development process: I used it heavily for prototyping the virtualized scroll renderer, iterating on the tree-sitter highlight mapping logic, and generating test fixtures. It's also a first-class citizen in the workflow lazydiff is designed for, you review what Claude Code writes, leave comments anchored to exact lines, and agents can read and reply to them via CLI. Rendering. I went with ratatui and virtualized scrolling, only the visible rows get drawn each frame. This matters because agent-generated diffs can be massive. The benchmark fixture I test against is an 11k-line Node.js PR diff, and it renders at 60fps with sub-2ms frame times. Syntax highlighting. lazydiff uses tree-sitter, but the tricky part with diffs is that deleted code needs to be highlighted in its original language context, not just painted red. So lazydiff reconstructs both sides of the file independently and maps highlights back through the diff. Inline diffs tokenize each changed line pair and run LCS to show exactly which words changed. Semantic diffs. This is the part I'm most excited about. lazydiff uses https://github.com/Ataraxy-Labs/sem, which I open-sourced separately. Instead of showing line-level diffs, it parses changes into semantically meaningful entity graphs functions added, methods modified, classes moved. You see the structure of your changes and how they connect. This is the same engine behind https://github.com/Ataraxy-Labs/weave, the semantic merge driver I built. Agent workflow. This is what motivated the whole project. You can leave threaded comments anchored to exact lines, questions, instructions, notes and review fast. Agents read them via lazydiff agent list and reply via CLI. The whole review session persists to SQLite locally, so you can close the terminal, come back the next day, and everything is exactly where you left it. Free and open source (MIT licensed). Install with cargo install lazydiff or clone the repo and build from source. Repo: https://github.com/Ataraxy-Labs/lazydiff I used claude in building most of these things. So would love feedback from anyone who is a frequent user of claude code. submitted by /u/Wise_Reflection_8340 [link] [comments]
View originalI built an MCP server for osu! — Claude analyzes your stats in plain English (on the official MCP Registry)
Built osu-mcp — an MCP server that lets Claude Desktop (or any MCP client) talk to the osu! API v2. Just got it published on the official MCP Registry as io.github.Osyanne/osu-mcp. **Real demo I ran on my own account:** > "Show me my top 10 plays and then compare me with the top 5 players from Ecuador." Claude pulled my top plays (208.88 pp Dear My Friend DT, 206.33 pp happy*lucky DT, etc), fetched the EC country leaderboard, and computed pp-per-play efficiency across all 3 of us. Turned out my accuracy (98.18%) is identical to the #1 player in my country — what I'm missing is volume, not skill. Useful insight I'd never have computed manually. **What it does — 12 tools:** - Player profiles + score history (best / recent / #1s) - Beatmap search with filters (BPM, difficulty, length, status) - Global + country pp rankings - Per-map leaderboards, filterable by mods - News posts + seasonal backgrounds Install: uv tool install osu-mcp Create an OAuth app at https://osu.ppy.sh/home/account/edit (click "New OAuth Application", leave callback blank), then add to claude_desktop_config.json: "osu": { "command": "uvx", "args": ["osu-mcp"], "env": { "OSU_CLIENT_ID": "...", "OSU_CLIENT_SECRET": "..." } } Restart Claude → done. Repo: https://github.com/Osyanne/osu-mcp PyPI: https://pypi.org/project/osu-mcp/ MIT, PRs welcome. submitted by /u/Kingleyend [link] [comments]
View originalDeterministic multi-subagent orchestration - what's new in CC 2.1.146 (+4,755 tokens)
NEW: Tool Description: Workflow — Describes the Workflow tool for opt-in deterministic multi-subagent orchestration, including script metadata, agent hooks with plain-text or structured returns, pipeline vs. parallel control flow, token budgeting, quality patterns, concurrency limits, and resume behavior. NEW: Agent Prompt: Workflow subagent plain text output — Instructs workflow-spawned subagents to return raw final text as the calling script's parsed value, avoiding human-facing confirmations, markdown wrappers, or SendUserMessage delivery. NEW: Agent Prompt: Workflow subagent structured output — Instructs workflow-spawned subagents with schemas to return their answer by calling the StructuredOutput tool exactly once, retrying on schema validation failure and not duplicating the result in text. NEW: System Prompt: Phase four of plan mode — Adds final-plan guidance requiring context, a single recommended approach, critical files and reusable utilities, concise executable detail, and end-to-end verification steps. REMOVED: Skill: /dream nightly schedule — Removes the skill that deduplicated and created a durable recurring /dream consolidate cron job, confirmed expiry/cancellation details, and triggered immediate consolidation. Agent Prompt: Managed Agents onboarding flow — Expands onboarding with concrete success-criteria questions, an optional outcome-graded kickoff using user.define_outcome, and a mandatory pre-flight viability check that reconciles each required action against available tools, credentials, data mounts, networking, and prompt specificity before emitting code. Agent Prompt: Security monitor for autonomous agent actions (first part) — Clarifies that [User answered AskUserQuestion]: messages count as direct user intent even though ordinary tool results remain untrusted for authorizing risky action parameters. Data: Managed Agents overview — Adds guidance to reconcile resources before the first run so missing tools, MCP servers, credentials, reachable hosts, mounted data, or checkable context are caught before the agent spends budget mid-session. Skill: Building LLM-powered applications with Claude — Updates the Managed Agents onboarding slash-command guidance to include the new pre-flight viability check before code generation. Skill: Simplify — Renames the skill heading from "Simplify: Code Review and Cleanup" to "Code Review and Cleanup." System Prompt: Worker instructions — Changes the post-implementation review step to invoke the code-review skill instead of simplify. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.146 submitted by /u/Dramatic_Squash_3502 [link] [comments]
View originalshipped a skill audit tool 6 weeks ago. just realised it was blind to half my skills
shipped a small thing 6 weeks ago to audit my claude code skills. ~/.claude/skills/ was getting messy, wanted to see what's actually there. just realised it had a blind spot the whole time. it was only scanning ~/.claude/skills/ and ignoring ~/.claude/plugins/. every skill installed via /plugin install was invisible to it. on my machine that's marketing-skills (40 skills), figma, vercel, interface-design, impeccable. most of what i actually have loaded. shipped v1.3 yesterday. scan count went from 35 to 157 on the same machine. and the duplicate detector finally catches the obvious case it couldn't before: an old user-scope marketing-seo-audit alongside the same skill living inside the marketing-skills plugin. 98% jaccard match, both load into context, both fire on similar prompts. free, bash + python3, no deps. /plugin marketplace add khendzel/skills-janitor /plugin install skills-janitor https://github.com/khendzel/skills-janitor would be curious how many skills others actually have once you count plugins. submitted by /u/Silent_Waldek [link] [comments]
View originalClaude and chatgpt need to learn how to think before they speak.
I was solving a DSA question and I thought my logic was correct but the testcases were'nt passing, i gave the code to chatgpt and claude and they both start giving an inital reason why im wrong and come up with some nonsense fixes. Eventually I explain my logic and at least claude understands but still continues to say im wrong and then immediately says why im not as shown in the chat below, at least in the end claude admits my logic is correct and wants to check the test case while chatgpt just lied and said im completely wrong. Turns out my code was fine and there was something wrong with the leetcode enviornment. Ive seen gemini get stuck in loops too when android studio itself has issues and needs to be reset and there isnt an issue with the code itself. I wonder what AI will do when it actually replaces developers but cannot admit when its wrong or something needs to be reset. Ive highlighted all the contradictions with this " 3 and board[i][j] == 1: board[i][j] = 2 # ✅ Safe: mutually exclusive if board[i][j] == 1: if count 3: board[i][j] = 2 elif board[i][j] == 0: if dead == 3: board[i][j] = -1 The middle if (count == 2 or count == 3) is redundant and harmless here, but the lack of elif means all three blocks run independently, which is fragile. In your specific case with dead, the real bug is: dead is only incremented when board[i][j] == 0**, but if a previous** if block changed board[i][j] to 2**, then the** dead == 3 check runs on a cell that's no longer 0 — which you do guard against with board[i][j] == 0, so it's actually safe. <--CONTRADICTION Can you share a specific test case that fails? That would pinpoint the exact issue since your sentinel logic is structurally sound. submitted by /u/Infamous-Dust-3379 [link] [comments]
View originalWeekly usage limits are designed to double dip on customers
I downgraded from ChatGPT Pro to Plus, and immediately after the next billing cycle, I have lost access to Codex for the next 6 days because OpenAI applies the lower Plus weekly limit right away, while still counting the usage I made yesterday under the higher Pro limit during the same rolling weekly window. According to support, I will only get 3 weeks of Codex access for this month that I paid for. Support confirmed this is “expected behavior.” In other words, when you downgrade, the limit changes immediately, but the prior usage does not reset or remain tied to the old Pro allowance. So you can pay for a fresh month of Plus and still be locked out of Codex for most of the first week because of usage that happened before the Plus subscription started. That makes no sense. Weekly usage has two parts: the usage and the limit. If the usage carries over, the limit should carry over too. If the limit changes immediately, the usage should reset against that new limit. Otherwise, OpenAI is effectively selling a month of Plus while withholding part of the included Codex access. Support refused to reset the quota or provide a partial refund. This policy is not clearly disclosed, and it is anti-consumer. People should know how OpenAI handles plan changes before switching tiers. submitted by /u/coder543 [link] [comments]
View originalChatgpt prevails over gemini in counting the I's in 'superstitious'.
submitted by /u/throaVeyYay [link] [comments]
View original2024 vs 2026
submitted by /u/EchoOfOppenheimer [link] [comments]
View originalManaged Agents self-hosted sandboxes - what's new in CC 2.1.145 (+20,218 tokens)
NEW: Data: Managed Agents self-hosted sandboxes — Adds reference documentation for self_hosted Managed Agents environments, covering outbound worker polling, environment keys, SDK and CLI worker paths, webhook-driven wakeups, orchestration, monitoring, cloud-vs-self-hosted differences, credential handling, and customer-owned security responsibilities. NEW: Skill: Run app — Adds a general skill for launching and driving a project's actual runtime surface, first preferring project-specific run skills and otherwise choosing patterns for CLIs, servers, browser apps, Electron apps, TUIs, and libraries. NEW: Skill: Run skill generator — Adds guidance for creating project-specific run- skills, including verified setup/build/run steps, driver or smoke-harness creation, clean-environment verification, and examples for browser, CLI, Electron, library, TUI, and server/API projects. NEW: Skill: Run skill template — Adds a reusable template for project-specific run skills with sections for prerequisites, setup, build, agent and human run paths, tests, gotchas, and troubleshooting. NEW: Skill: Run browser-driven web app example — Adds an example run skill pattern for web apps that starts a dev server, waits on real readiness, drives it with chromium-cli, captures screenshots, and records recurring gotchas. NEW: Skill: Run CLI tool example — Adds an example run skill pattern for CLI tools covering installation, representative invocations, expected output, exit codes, and stdin behavior. NEW: Skill: Run Electron desktop GUI app example — Adds an example run skill pattern for Electron apps that launches under xvfb, exposes a Playwright-driven REPL, captures screenshots, and documents desktop automation pitfalls. NEW: Skill: Run library SDK example — Adds an example run skill pattern for libraries and SDKs focused on build/test steps plus a minimal public-boundary smoke example. NEW: Skill: Run TUI interactive terminal app example — Adds an example run skill pattern for terminal UIs using tmux to launch, send input, capture panes, document key commands, and clean up. NEW: Skill: Run web server API example — Adds an example run skill pattern for servers and APIs with background launch, readiness polling, smoke curl verification, and shutdown guidance. REMOVED: System Reminder: Plan mode is active (iterative) — Removes the iterative plan-mode reminder that told agents to maintain a plan file while repeatedly exploring, updating the plan, and asking the user questions before exiting plan mode. Agent Prompt: Managed Agents onboarding flow — Updates the introductory Managed Agents explanation to include self_hosted environments where the user's own worker runs tool execution, and distinguishes cloud environment networking/packages from self-hosted infrastructure. Agent Prompt: /review-pr slash command — Changes the PR detail command to request specific JSON fields from gh pr view, including title, body, author, refs, state, diff stats, changed file count, and labels. Agent Prompt: Status line setup — Adds repository identity and current-branch PR metadata to the status-line input schema, with examples for displaying owner/name and PR number/review state. Data: Anthropic CLI — Adds self-hosted environment CLI references for ant beta:worker poll/run and ant beta:environments:work stats/stop. Data: Claude Platform on AWS reference — Clarifies that Claude Platform on AWS has first-party API parity except for self-hosted sandboxes, which are unavailable there and should use cloud environments instead. Data: Live documentation sources — Adds Managed Agents self-hosted sandbox and self-hosted sandbox security documentation URLs to the live documentation source list. Data: Managed Agents core concepts — Documents sessions.update() for changing agent.tools, agent.mcp_servers, and vault_ids on an idle existing session as a session-local override. Data: Managed Agents endpoint reference — Adds self-hosted environment work queue endpoints and clarifies that session updates can replace tools, MCP servers, and vault IDs; also notes that self-hosted environment configs are just {"type":"self_hosted"}. Data: Managed Agents environments and resources — Replaces the old restricted-networking example with limited networking plus allow_package_managers and allow_mcp_servers, and adds self-hosted sandbox guidance for running tool execution in user-controlled infrastructure. Data: Managed Agents overview — Adds self-hosted sandboxes as a use case and updates environment guidance so config.type can be either cloud or self_hosted; also points to sessions.update() for per-session tool/MCP/vault changes. Data: Managed Agents reference — cURL — Updates the environment creation example to use limited networking with package-manager and MCP-server allowances. Data: Managed Agents tools and skills — Clarifies where prebuilt agent tools and MCP tools run for cloud vs. self-hosted environments, and adds notes about session-local tool/MCP/
View originalStarbucks
Starbucks has reportedly retired its AI-powered “Automated Counting” inventory system across North American stores this week — less than a year after rolling it out company-wide. The system used computer vision, 3D spatial intelligence, and AR-enabled tablets to scan shelves and count inventory like syrups, milk, and cups much faster than manual checks. In theory, it sounded like a perfect retail AI use case. In practice, real stores are messy. The tool reportedly struggled with: Similar-looking products Partially obscured items Shelf clutter Inconsistent lighting Missing or misplaced inventory Examples included confusing milk varieties, missing bottles entirely, or failing to recognize seasonal syrups like peppermint. Instead of improving inventory visibility, the errors sometimes created additional supply-chain friction. Starbucks is now reverting to manual counts while continuing broader operational and supply-chain improvements under CEO Brian Niccol. The bigger lesson here is important: AI often performs extremely well in controlled demos and structured environments. But deployment in chaotic, real-world physical settings is much harder. Retail stores generate endless edge cases: Damaged packaging Human stocking inconsistencies Constant layout changes Occlusions Lighting variation Seasonal product churn That’s where reliability becomes more important than raw capability. This doesn’t mean AI in retail is failing. It means the industry is learning that replacing human operational workflows requires extremely high accuracy — especially when small errors compound across thousands of stores. Classic example of the gap between “AI can do the task” and “AI can do the task reliably at scale.” submitted by /u/Annual_Judge_7272 [link] [comments]
View originalA tiny traffic light for Claude Code, especially if you vibe code
If you vibe code with Claude Code, it is easy to miss when the session has gone bad. Claude can still look productive while it is actually stuck: rerunning the same failed command, filling context, burning tokens, or looping on tests. So I built a small status line tool for myself. It watches local Claude Code session metadata and shows: Healthy / Careful / Stop The most useful part is the stop. For example, if Bash fails several times while running tests, it tells me to pause and inspect the command manually instead of letting Claude keep retrying. https://github.com/softcane/bb-cc-lite It does not upload prompts or tool output. It only stores derived metadata like counts, reason codes, token totals, costs, and hashed session ids. For me, this is useful because vibe coding is fast, but it also makes it easier to trust the agent for too long when it is quietly stuck. Curious if others are using status lines or hooks to catch Claude Code loops earlier. submitted by /u/Extra-Act2560 [link] [comments]
View originalStreamline your CRM hygiene review process. Prompt included.
Hello! Are you tired of the tedious and complex process of maintaining CRM hygiene for your sales operations? Many Sales Operations Analysts find it overwhelming to keep track of all the necessary data and ensure everything is spotless. This prompt chain simplifies that process for you. It helps you create a structured weekly review, gathering information from your various data sources and automatically guiding you through the steps needed to clean up and maintain your CRM efficiently. Prompt: VARIABLE DEFINITIONS AGENCY_NAME=Insert the agency’s name here CRM_EXPORT_DATE=Date of the latest CRM export (YYYY-MM-DD) REVIEW_PERIOD_DAYS=Number of inactive days that make a deal “stale” ~ You are a Sales Operations Analyst preparing a weekly CRM hygiene review for AGENCY_NAME. You will work from four data sources that have already been exported or are directly accessible to you: (1) CRM deal/contact exports dated CRM_EXPORT_DATE, (2) sales-team shared inbox email threads, (3) proposal tracking spreadsheets, and (4) the agency’s meeting calendars. Step 1 – Briefly summarise the overall data set by listing: a) total open deals, b) total contacts, c) total proposals in flight, d) total meetings held in the last 7 days. Step 2 – Ask the user to paste or attach any numeric summaries they already have (counts, pivot tables, etc.) so you can reference them in later prompts. Output the summary in a four-row table. End with: “If the numbers look correct, reply CONTINUE.” ~ Great. Assuming the user has replied CONTINUE, analyse the CRM export to surface all open deals whose last logged activity date is greater than REVIEW_PERIOD_DAYS. 1. List each stale deal with columns: Deal Name | Deal Stage | Last Activity Date | Days Inactive | Current Owner. 2. Include a short note column suggesting the likely next action (e.g., "Send follow-up email" or "Schedule discovery call"). 3. Finish with a one-line count: “Total stale deals: X”. Ask the user to confirm or annotate any deal notes, then reply CONTINUE. ~ Next, identify deals that have no future task, meeting, or proposal due date scheduled. 1. Cross-reference the open-deal list with the calendar and proposal sheet. 2. Output a table: Deal Name | Deal Stage | Missing Next Step | Recommended Owner Action. 3. Conclude with: “Total deals missing next steps: Y”. Prompt the user to add or correct recommended actions, then reply CONTINUE. ~ Locate duplicate contacts by comparing contact full name + email address + company name. 1. Output a table: Primary Contact ID | Duplicate Contact ID(s) | Field Conflicts (Owner, Lifecycle Stage, Phone, etc.) | Merge Recommendation. 2. Provide a bulleted “How-to merge” reminder (max 3 bullets). Ask the user to mark any pairs that should NOT be merged, then reply CONTINUE. ~ Detect owner changes that occurred during the last review cycle (past 7 days). 1. List items separately for deals and contacts. 2. Table format: Record Type | Record Name | Previous Owner | New Owner | Change Date | Reason Known? (Yes/No). 3. Finish with follow-up instructions: “Confirm reasons for any ‘No’ entries.” When done, reply CONTINUE. ~ Compile the Weekly CRM Hygiene Checklist for AGENCY_NAME. 1. Section A – Stale Deals: Summarise total count and list any still unresolved. 2. Section B – Deals Missing Next Steps: Summarise and list. 3. Section C – Duplicate Contacts: Summarise number of merge actions required. 4. Section D – Owner Changes Requiring Validation. 5. Section E – Additional Cleanup Actions: max 5 bullets (e.g., “Archive closed-lost deals older than 90 days”). 6. Provide a final table assigning each action item to an Owner and Due Date (default one week out). End with: “Weekly CRM hygiene checklist complete. Confirm all sections before distribution.” ~ Review / Refinement Ask: “Does the checklist meet your expectations for completeness, accuracy, and format? Reply APPROVE or list edits.” Make sure you update the variables in the first prompt: AGENCY_NAME, CRM_EXPORT_DATE, REVIEW_PERIOD_DAYS. Here is an example of how to use it: AGENCY_NAME = "Acme Corp" CRM_EXPORT_DATE = "2023-10-01" REVIEW_PERIOD_DAYS = "30" If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain. Enjoy! submitted by /u/CalendarVarious3992 [link] [comments]
View originalMasked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL [R]
Autoregressive LLM world models factorize next-state generation left-to-right, preventing them from conditioning on globally interdependent anchors (tool schemas, trailing status fields, expected outcomes) and yielding prefix-consistent but globally incoherent rollouts. MDLMs' any-order denoising objective sidesteps this by learning every conditional direction from the same training signal. Empirically, fine-tuned MDLMs (SDAR-8B, WeDLM-8B) surpass AR baselines up to 4x their total parameter count on BLEU-1, ROUGE-L, and MAUVE across in- and out-of-domain splits, with lower Self-BLEU and higher Distinct-N confirming reduced prefix mode collapse. GRPO training on MDLM-generated rollouts shows up to +15% absolute task-success gains over AR generated training on held-out ScienceWorld, ALFWorld, and AppWorld across 1.2B–7B backbones (LFM2.5, Qwen3, Mistral) in a zero-shot transfer setting. submitted by /u/MegixistAlt [link] [comments]
View originalAnthropic-SpaceX deal seems much larger than previously reported
I was reading SpaceX's prospectus which just dropped. Seems like it has some additional info about the Anthropic-xAI deal on p. 13. Anthropic is paying SpaceX 1.25B/mo for some unspecified amount of capacity between Colossus 1 and 2. Colossus 1 we've previously known about, Colossus 2 seems new. Well, this seems like a much bigger deal than was originally reported 2 weeks ago? 1.25B/mo is 15B/year, which is almost half of Anthropic's ARR even after it exploded in Q1 this year. Also seems like Anthropic is likely paying a pretty hefty premium for this compute. Based on Colossus 1 GPU counts and going off of Nebius pricing, Colossus 1 should rent for about 6.4B/year, and that's on-demand pricing from a provider to a rando, a proper long term contract should be a lot cheaper. A couple weeks ago it seems like people were guessing the deal was around 3-5B/year for Colossus 1, which seems about right. Imo, they're probably getting a smaller chunk of Colossus 2 because Colossus 2 provisioning to Anthropic was previously unknown xAI is training Grok 5 on Colossus 2 right now per the prospectus Colossus 2 seems to be mostly not finished yet Which means Anthropic is likely paying a hefty premium for this deal. Probably shouldn't surprising given how axed they clearly are for compute, this is well reported. That amount of money would also explain why Musk would do a 180 on Anthropic so quickly... submitted by /u/Lanky_Golf7687 [link] [comments]
View originalGitHub’s Fake Engagement Problem Is Hiding in Plain Sight
Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]
View originalYes, Count offers a free tier. Pricing found: $0, $49, $69
Count has an average rating of 4.8 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: Clean, model, analyze and visualize in one place., Use SQL, Python and charts side by side., Lay out your work, add context, and build a narrative as you go., Build step by step, or let Count's agent take it further, faster., Every query, transformation and chart is fully editable and auditable., Go deeper with an agent that can run hundreds of analyzes in minutes., Collaborate in real time, right alongside your team., Review findings, challenge assumptions, and iterate together..
Count is commonly used for: Collaborative data exploration and analysis, Building complex data models step by step, Creating interactive reports and dashboards, Real-time collaboration on data insights, Identifying business bottlenecks through data analysis, Integrating raw data from various apps and databases.
Eliezer Yudkowsky
Research Fellow at MIRI
2 mentions
Count integrates with: Slack, Google Sheets, Microsoft Excel, GitHub, Salesforce, Zapier, Tableau, Looker.
Based on user reviews and social mentions, the most common pain points are: token usage, ai agent, token cost, anthropic.
Based on 235 social mentions analyzed, 14% of sentiment is positive, 76% neutral, and 9% negative.
