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I cannot provide a meaningful summary about "Count" software based on the provided content. The social mentions you've shared appear to be unrelated political and technology news articles from platforms like Lemmy, Hacker News, and TikTok, but none specifically discuss or review a software tool called "Count." Additionally, no actual user reviews were provided in the reviews section. To summarize user sentiment about Count, I would need relevant reviews and social mentions that actually discuss the software's features, performance, pricing, and user experience.
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I cannot provide a meaningful summary about "Count" software based on the provided content. The social mentions you've shared appear to be unrelated political and technology news articles from platforms like Lemmy, Hacker News, and TikTok, but none specifically discuss or review a software tool called "Count." Additionally, no actual user reviews were provided in the reviews section. To summarize user sentiment about Count, I would need relevant reviews and social mentions that actually discuss the software's features, performance, pricing, and user experience.
Features
Industry
information technology & services
Employees
28
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
CrowdStrike, Cisco and Palo Alto Networks all shipped agentic SOC tools at RSAC 2026 — the agent behavioral baseline gap survived all three
CrowdStrike CEO George Kurtz highlighted in his RSA Conference 2026 keynote that the fastest recorded adversary breakout time has dropped to 27 seconds. The average is now 29 minutes, down from 48 minutes in 2024. That is how much time defenders have before a threat spreads. Now CrowdStrike sensors detect more than 1,800 distinct AI applications running on enterprise endpoints, representing nearly 160 million unique application instances. Every one generates detection events, identity events, and data access logs flowing into SIEM systems architected for human-speed workflows. Cisco found that 85% of surveyed enterprise customers have AI agent pilots underway. Only 5% moved agents into production, according to Cisco President and Chief Product Officer Jeetu Patel in his RSAC blog post. That 80-point gap exists because security teams cannot answer the basic questions agents force. Which agents are running, what are they authorized to do, and who is accountable when one goes wrong. “The number one threat is security complexity. But we’re running towards that direction in AI as well,” Etay Maor, VP of Threat Intelligence at Cato Networks, told VentureBeat at RSAC 2026. Maor has attended the conference for 16 consecutive years. “We’re going with multiple point solutions for AI. And now you’re creating the next wave of security complexity.” Agents look identical to humans in your logs In most default logging configurations, agent-initiated activity looks identical to human-initiated activity in security logs. “It looks indistinguishable if an agent runs Louis’s web browser versus if Louis runs his browser,” Elia Zaitsev, CTO of CrowdStrike, told VentureBeat in an exclusive interview at RSAC 2026. Distinguishing the two requires walking the process tree. “I can actually walk up that process tree and say, this Chrome process was launched by Louis from the desktop. This Chrome process was launched from Louis’s Claude Cowork or ChatGPT application. Thus, it’s agentically con
View originalClaude Code's source code appears to have leaked: here's what we know
Anthropic appears to have accidentally revealed the inner workings of one of its most popular and lucrative AI products, the agentic AI harness Claude Code, to the public. A 59.8 MB JavaScript source map file (.map), intended for internal debugging, was inadvertently included in version 2.1.88 of the @anthropic-ai/claude-code package on the public npm registry pushed live earlier this morning. By 4:23 am ET, Chaofan Shou (@Fried_rice), an intern at Solayer Labs, broadcasted the discovery on X (formerly Twitter). The post, which included a direct download link to a hosted archive, acted as a digital flare. Within hours, the ~512,000-line TypeScript codebase was mirrored across GitHub and analyzed by thousands of developers. For Anthropic, a company currently riding a meteoric rise with a reported $19 billion annualized revenue run-rate as of March 2026, the leak is more than a security lapse; it is a strategic hemorrhage of intellectual property.The timing is particularly critical given the commercial velocity of the product. Market data indicates that Claude Code alone has achieved an annualized recurring revenue (ARR) of $2.5 billion, a figure that has more than doubled since the beginning of the year. With enterprise adoption accounting for 80% of its revenue, the leak provides competitors—from established giants to nimble rivals like Cursor—a literal blueprint for how to build a high-agency, reliable, and commercially viable AI agent. Anthropic confirmed the leak in a spokesperson’s e-mailed statement to VentureBeat, which reads: “Earlier today, a Claude Code release included some internal source code. No sensitive customer data or credentials were involved or exposed. This was a release packaging issue caused by human error, not a security breach. We're rolling out measures to prevent this from happening again.” The anatomy of agentic memory The most significant takeaway for competitors lies in how Anthropic solved "context entropy"—the tendency for AI agents to
View originalThe company behind ClassPass and Mindbody just got a lot bigger with a $7.5B merger
The merger is a sign that the fitness industry is continuing to move toward consolidation to compete at a larger scale. Recent moves include MyFitnessPal acquiring Cal AI, an AI calorie counting app, and Strava buying two apps: cycling app The Breakaway and running app Runna.
View original7,655 Ransomware Claims in One Year: Group, Sector, and Country Breakdown
View originalGlia wins Excellence Award for safer AI in banking
Glia, a customer service platform providing AI-powered interactions for the banking sector, has been named a winner in the Banking and Financial Services Category at the 2026 Artificial Intelligence Excellence Awards. The awards recognises achievements in a range of industries and use cases, spotlighting “companies and leaders moving AI beyond experimentation and into practical, accountable […] The post Glia wins Excellence Award for safer AI in banking appeared first on AI News.
View originalWhen AI turns software development inside-out: 170% throughput at 80% headcount
Many people have tried AI tools and walked away unimpressed. I get it — many demos promise magic, but in practice, the results can feel underwhelming. That’s why I want to write this not as a futurist prediction, but from lived experience. Over the past six months, I turned my engineering organization AI-first. I’ve shared before about the system behind that transformation — how we built the workflows, the metrics, and the guardrails. Today, I want to zoom out from the mechanics and talk about what I’ve learned from that experience — about where our profession is heading when software development itself turns inside out. Before I do, a couple of numbers to illustrate the scale of change. Subjectively, it feels that we are moving twice as fast. Objectively, here’s how the throughput evolved. Our total engineering team headcount floated from 36 at the beginning of the year to 30. So you get ~170% throughput on ~80% headcount, which matches the subjective ~2x. Zooming in, I picked a couple of our senior engineers who started the year in a more traditional software engineering process and ended it in the AI-first way. [The dips correspond to vacations and off-sites]: Note that our PRs are tied to JIRA tickets, and the average scope of those tickets didn’t change much through the year, so it’s as good a proxy as the data can give us. Qualitatively, looking at the business value, I actually see even higher uplift. One reason is that, as we started last year, our quality assurance (QA) team couldn’t keep up with our engineers' velocity. As the company leader, I wasn’t happy with the quality of some of our early releases. As we progressed through the year, and tooled our AI workflows to include writing unit and end-to-end tests, our coverage improved, the number of bugs dropped, users became fans, and the business value of engineering work multiplied. From big design to rapid experimentation Before AI, we spent weeks perfecting user flows before writing code. It made sense
View originalShow HN: Forkrun – NUMA-aware shell parallelizer (50×–400× faster than parallel)
forkrun is the culmination of a 10-year-long journey focused on "how to make shell parallelization fast". What started as a standard "fork jobs in a loop" has turned into a lock-free, CAS-retry-loop-free, SIMD-accelerated, self-tuning, NUMA aware shell-based stream parallelization engine that is (mostly) a drop-in replacement for xargs -P and GNU parallel.<p>On my 14-core/28-thread i9-7940x, forkrun achieves:<p>* 200,000+ batch dispatches/sec (vs ~500 for GNU Parallel)<p>* ~95–99% CPU utilization across all 28 logical cores, even when the workload is non-existant (bash no-ops / `:`) (vs ~6% for GNU Parallel). These benchmarks are intentionally worst-case (near-zero work per task) because they measure the capability of the parallelization framework itself, not how much work an external tool can do.<p>* Typically 50×–400× faster on real high-frequency low-latency workloads (vs GNU Parallel)<p>A few of the techniques that make this possible:<p>* Born-local NUMA: stdin is splice()'d into a shared memfd, then pages are placed on the target NUMA node via set_mempolicy(MPOL_BIND) before any worker touches them, making the memfd NUMA-spliced. Each numa node only claims work that is <i>already</i> born-local on its node. Stealing from other nodes is permitted under some conditions when no local work exists.<p>* SIMD scanning: per-node indexers/scanners use AVX2/NEON to find line boundaries (delimiters) at speeds approaching memory bandwidth, and publish byte-offsets and line-counts into per-node lock-free rings.<p>* Lock-free claiming: workers claim batches with a single atomic_fetch_add — no locks, no CAS retry loops; contention is reduced to a single atomic on one cache line.<p>* Memory management: a background thread uses fallocate(PUNCH_HOLE) to reclaim space without breaking the logical offset system.<p>…and that’s just the surface. The implementation uses many additional systems-level techniques (phase-aware tail handling, adaptive batching, early-flush detection, etc.) to eliminate overhead, increase throughput and reduce latency at every stage.<p>In its fastest (-b) mode (fixed-size batches, minimal processing), it can exceed 1B lines/sec.<p>forkrun ships as a single bash file with an embedded, self-extracting C extension — no Perl, no Python, no install, full native support for parallelizing arbitrary shell functions. The binary is built in public GitHub Actions so you can trace it back to CI (see the GitHub "Blame" on the line containing the base64 embeddings). Trying it is literally two commands:<p><pre><code> . frun.bash frun shell_func_or_cmd < inputs </code></pre> For benchmarking scripts and results, see the BENCHMARKS dir in the GitHub repo<p>For an architecture deep-dive, see the DOCS dir in the GitHub repo<p>Happy to answer questions.
View originalCountry that put backdoors in Cisco routers to spy on world bans foreign routers
View originalLocal Stack Archived their GitHub repo and requires an account to run
View originalAdversarial Attacks and Defenses in Deep Learning Systems: Threats, Mechanisms, and Countermeasures
Hello y'all, I'm back again in 2026🔥🔥 Last Wednesday I just had the opportunity to join in the...
View originalThe accessibility gap: Why good intentions aren’t enough for digital compliance
Presented by AudioEye While most organizations recognize the importance of accessibility from a theoretical angle, a stark gap exists between that awareness and actual execution. Companies can't just give a nod to accessibility -- and it can't just be a nice-to-have. The chasm between knowing and doing is not only exposing businesses to significant legal risk, it's also costing them actual business and growth opportunities. According to AudioEye’s newly released 2026 Accessibility Advantage Report, 59% of business leaders say their organization would face legal risk due to accessibility failure if audited today, and more than half have already encountered accessibility-related lawsuits or threats. That’s unsurprising, because today the average web page still contains 297 accessibility issues, based on an analysis of over 15,000 websites in AudioEye’s 2025 Digital Accessibility Index. The report, which surveyed more than 400 business leaders across the C-suite, VPs, and directors, reveals that organizations understand accessibility matters, but most lack the systems, expertise, and operational infrastructure to deliver it consistently, says Chad Sollis, CMO at AudioEye. “What the data makes clear is that accessibility hasn’t stalled because people don’t care,” Sollis says. “It’s stalled because fragmented ownership and reactive workflows make it hard to sustain as digital experiences evolve. Leaders know accessibility matters, but their organizations aren’t set up to deliver it consistently.” Why digital accessibility delivers a measurable business advantage With regulations like the European Accessibility Act now in effect and enforcement intensifying globally, the benefits extend far beyond avoiding lawsuits. Over half of leaders now cite accessibility as a business growth opportunity, recognizing that accessible digital experiences drive better user outcomes across the board. “Organizations that treat accessibility purely as a compliance exercise miss the opportun
View originalFixing AI failure: Three changes enterprises should make now
Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical. Internal projects that struggle tend to share common issues. For example, engineering teams build models that product managers don’t know how to use. Data scientists build prototypes that operations teams struggle to maintain. And AI applications sit unused because the people they were built for weren't involved in deciding what “useful” really meant. In contrast, organizations that achieve meaningful value with AI have figured out how to create the right kind of collaboration across departments, and established shared accountability for outcomes. The technology matters, but the organizational readiness matters just as much. Here are three practices I’ve observed that address the cultural and organizational barriers that can impede AI success. Expand AI literacy beyond engineering When only engineers understand how an AI system works and what it’s capable of, collaboration breaks down. Product managers can't evaluate trade-offs they don't understand. Designers can't create interfaces for capabilities they can't articulate. Analysts can't validate outputs they can't interpret. The solution isn't making everyone a data scientist. It's helping each role understand how AI applies to their specific work. Product managers need to grasp what kinds of generated content, predictions or recommendations are realistic given available data. Designers need to understand what the AI can actually do so they can design features users will find useful. Analysts need to know which AI outputs require human validation versus which can be trusted. When teams share this working vocabulary, AI stops being something that happens in
View original[Resource]: WRITE THE NAME OF YOUR RESOURCE HERE
### Display Name AI.MD ### Category Agent Skills ### Sub-Category General ### Primary Link https://github.com/sstklen/ai-md ### Author Name sstklen ### Author Link https://github.com/sstklen ### License MIT ### Other License _No response_ ### Description Converts human-written CLAUDE.md files into AI-native structured-label format using a 6-phase methodology (understand, decompose, label,structure, resolve, test). Battle-tested with 4 LLM models — structured format raised Codex (GPT-5.3) compliance from 6/8 to 8/8 on identical rule content, while reducing file size by 53% and line count by 37% (224 → 142 lines, within Claude Code's recommended 200-line limit). ### Validate Claims Install the skill and run it on any CLAUDE.md over 100 lines. The skill measures before/after byte count and line count, converts to structured-label format with automatic backup (.bak), and reports the diff. Real test data is in the README (4-model comparison table). The examples/ directory contains a complete before/after pair for manual inspection. ### Specific Task(s) Have Claude Code convert an existing CLAUDE.md using the AI.MD skill. Then compare compliance by testing both versions (original backup vs converted) with the same set of questions. The skill's SKILL.md documents the exact 8-question test protocol used in validation. ### Specific Prompt(s) Say: "distill my CLAUDE.md" or "AI.MD" — the skill previews current token cost, shows before/after examples, then offers to convert with full backup. After conversion, say "test my CLAUDE.md" to run the built-in multi-model validation. ### Additional Comments The core insight: LLMs re-read CLAUDE.md every conversation turn. Human prose wastes tokens and splits attention across rules sharing a line. Structured-label format (one concept per line, explicit trigger/action/exception labels, XML section boundaries) gives each rule full attention weight. This is not compression — it's restructuring the same rules into a format LLMs parse more reliably. Full methodology: 6 conversion phases + 5 special techniques documented in SKILL.md (525 lines). ### Recommendation Checklist - [x] I have checked that this resource hasn't already been submitted - [x] It has been over one week since the first public commit to the repo I am recommending - [x] All provided links are working and publicly accessible - [x] I do NOT have any other open issues in this repository - [x] I am primarily composed of human-y stuff and not electrical circuits
View originalWe Need to Stop Listening to Tony Blair Once and for All
 It might feel like months, but we’re just over a week into the US and Israel’s illegal assault on Iran, and there’s no end in sight. What is in sight, though, is [the apocalyptic vision of Tehran ablaze](https://time.com/7383099/iran-news-oil-strikes-tehran/), wreathed in thick smoke as black oil-soaked rain falls on its inhabitants. That’s the result of Israeli strikes on several oil storage depots in the city, reportedly sending burning petroleum running through gutters while [geysers of flaming gas exploded](https://www.telegraph.co.uk/world-news/2026/03/08/rivers-of-fire-in-tehran-after-oil-depots-blown-up/) from the streets. A nightmare? For most of us, yes. But for former British prime minister Tony Blair it’s apparently a dream. One that he might have liked the entire British public to be non-consensually forced into realising for him. And [not for the first time](https://www.bbc.co.uk/news/uk-politics-36701854). Were my hands bloodied with the [deaths of up to a million people](https://www.abc.net.au/news/2008-01-31/million-iraqis-dead-since-invasion-study/1028878), I’d probably think twice before giving my opinion on yet another illegal US adventure in the Middle East. Not our Tone, though. On Sunday [the papers reported](https://www.dailymail.co.uk/news/article-15623903/Tony-Blair-rebukes-Keir-Starmer-not-backing-Trump-Iran.html) that the man who told George W. Bush in the months before the disastrous Iraq war, “[I will be with you, whatever](https://www.theguardian.com/uk-news/2016/jul/06/with-you-whatever-tony-blair-letters-george-w-bush-chilcot)”, is still singing the same old tune. “We should,” Blair [told a private Jewish News event](https://www.independent.co.uk/news/uk/home-news/blair-starmer-trump-war-iran-labour-b2934207.html) on Friday night, “Have backed America from the very beginning”. That was a direct criticism of current prime minister Keir Starmer, who, [to a chorus of warmonger criticism](https://www.bbc.co.uk/news/articles/c05v28eqjyvo), initially refused the US and Israel access to British military infrastructure to launch its war on Iran. But it’s not like we’ve stayed completely out of the mess: our bases are now free for use by US jets for “defensive” actions – whatever that means – with American bombers [already touching down](https://www.theguardian.com/world/2026/mar/07/us-bomber-lands-in-uk-after-warning-of-surge-in-strikes-on-iran). Now, nobody was ever supposed to know that a former Labour prime minister so openly rubbished the current one in public. That’s because the event was conducted under Chatham House rules. In short, that means what’s said in the room can be made public, but not who said it. In long, it means elites are emboldened to express their heart’s true desires without any threat of accountability. We can’t know what was in Tony Blair’s heart when he mourned the fact that the UK was not more involved in blasting a hole straight through the security of the hundreds of millions who live in the Middle East. Nor can we tell for sure, as global oil prices [surge above $100 a barrel](https://www.bbc.co.uk/news/articles/c79542n0grwo) for the first time since the Russian invasion of Ukraine, how little the lives of Brits, long blighted by a cost of living crisis, matter to him. We can, though, look at his record. And what that shows – in my opinion – is a tendency, previously expressed via his businesses and [nowadays his Tony Blair Institute](https://www.ft.com/content/bcf1f1f5-a38f-4078-98f8-ab1ff7378895), to see fatal discord as fiscal opportunity. [Autocracy](https://www.theguardian.com/politics/2023/aug/12/tony-blair-institute-continued-taking-money-from-saudi-arabia-after-khashoggi), [oligarchy](https://www.theguardian.com/world/2022/jan/06/how-tony-blair-advised-former-kazakh-ruler-after-2011-uprising), [calamity](https://www.theguardian.com/politics/2025/jul/07/tony-blair-thinktank-worked-with-project-developing-trump-riviera-gaza-plan)? Roll up, roll up: the Blair pitch project is in town, and it has some consultancy to sell. Now, none of that is a crime. But you might think it indicates a conflict when wading into affairs of state. Blair is alleged to have form here too: in 2014, a number of former ambassadors and MPs [called for his resignation](https://www.theguardian.com/politics/2014/jun/27/tony-blair-conflict-interests-middle-east) as Middle East peace envoy for the Quartet (made up of the United Nations, the US, the EU and Russia). They claimed he was ineffective, while others noted [the growth of his business interests in the region](https://www.independent.co.uk/voices/tony-blair-uae-middle-east-envoy-qatar-israel-palestine-foreign-office-a7894641.html). Blair’s [financial arrangements](https://www.telegraph.co.uk/news/poli
View originalchore(pricing): Update vertex-ai pricing
## 🔄 Pricing Update: vertex-ai ### 📊 Summary (complete_diff mode) | Change Type | Count | |-------------|-------| | ➕ Models added | 70 | | 🔄 Models updated (merged) | 24 | ### ➕ New Models - `gemini-2.5-computer-use-preview-10-2025` - `gemini-2.5-flash-preview-09-2025` - `gemini-2.5-flash-lite-preview-09-2025` - `gemini-3.1-flash-lite-preview` - `imagen-3.0-generate-002` - `imagen-3.0-capability-002` - `imagen-product-recontext-preview-06-30` - `text-embedding-large-exp-03-07` - `multimodalembedding` - `gpt-oss` - `gpt-oss-120b-maas` - `whisper-large` - `mistral` - `mixtral` - `mistral-small-2503` - `codestral-2501-self-deploy` - `mistral-ocr-2505` - `mistral-medium-3` - `codestral-2` - `ministral-3` - ... and 50 more ### 🔄 Updated Models - `gemini-2.5-pro` - `gemini-2.5-flash` - `gemini-2.5-flash-lite` - `gemini-2.5-flash-image` - `gemini-2.5-flash-image-preview` - `gemini-3.1-pro-preview` - `gemini-3-pro-preview` - `gemini-3-pro-image-preview` - `imagen-4.0-generate-001` - `imagen-4.0-fast-generate-001` - `imagen-4.0-ultra-generate-001` - `imagen-4.0-generate-preview-06-06` - `imagen-4.0-fast-generate-preview-06-06` - `imagen-4.0-ultra-generate-preview-06-06` - `imagen-3.0-capability-001` - `veo-3.0-generate-001` - `veo-3.0-fast-generate-001` - `veo-3.0-generate-preview` - `veo-3.0-fast-generate-preview` - `veo-3.1-generate-001` - `veo-3.1-generate-preview` - `veo-3.1-fast-generate-preview` - `text-embedding-005` - `text-multilingual-embedding-002` ## Model-to-Pricing-Page Mapping | Model ID | Publisher / Section | Source | Notes | |----------|-------------------|--------|-------| | `gemini-2.5-pro` | Google – Gemini 2.5 | API | $1.25/$10 input/output (≤200K); cache read $0.125 | | `gemini-2.5-flash` | Google – Gemini 2.5 | API | $0.30/$2.50; cache $0.03; image_token $30/1M | | `gemini-2.5-flash-lite` | Google – Gemini 2.5 | API | $0.10/$0.40; cache $0.01 | | `gemini-2.5-flash-image` | Google – Gemini 2.5 | API | Same as gemini-2.5-flash with image output | | `gemini-2.5-flash-image-preview` | Google – Gemini 2.5 | API | Same as gemini-2.5-flash (preview alias) | | `gemini-2.5-computer-use-preview-10-2025` | Google – Gemini 2.5 | API | Matched as "Gemini 2.5 Pro Computer Use-Preview"; $1.25/$10, no cache | | `gemini-2.5-flash-preview-09-2025` | Google – Gemini 2.5 | API | Preview alias of gemini-2.5-flash; same pricing | | `gemini-2.5-flash-lite-preview-09-2025` | Google – Gemini 2.5 | API | Preview alias of gemini-2.5-flash-lite; same pricing | | `gemini-2.0-flash-001` | Google – Gemini 2.0 | API | $0.15/$0.60; batch $0.075/$0.30 | | `gemini-2.0-flash-lite-001` | Google – Gemini 2.0 | API | $0.075/$0.30; batch $0.0375/$0.15 | | `gemini-3.1-pro-preview` | Google – Gemini 3 | API | $2/$12; cache $0.2; web_search 1.4¢ | | `gemini-3-pro-preview` | Google – Gemini 3 | API | $2/$12; cache $0.2; web_search 1.4¢ | | `gemini-3-pro-image-preview` | Google – Gemini 3 | API | $2/$12; image_token $120/1M; web_search 1.4¢ | | `gemini-3.1-flash-image-preview` | Google – Gemini 3 | API | $0.50/$3; image_token $60/1M; web_search 1.4¢ | | `gemini-3.1-flash-lite-preview` | Google – Gemini 3 | API | $0.25/$1.50; cache $0.025; web_search 1.4¢ | | `gemini-3-flash-preview` | Google – Gemini 3 | API | $0.50/$3; cache $0.05; web_search 1.4¢ | | `imagen-4.0-generate-001` | Google – Imagen | API | Row matched via lookup_variant `imagen-4.0-generate`; $0.04/image | | `imagen-4.0-fast-generate-001` | Google – Imagen | API | Row matched via `imagen-4.0-fast-generate`; $0.02/image | | `imagen-4.0-ultra-generate-001` | Google – Imagen | API | Row matched via `imagen-4.0-ultra-generate`; $0.06/image | | `imagen-4.0-generate-preview-06-06` | Google – Imagen | API | Preview; matched as Imagen 4; $0.04/image | | `imagen-4.0-fast-generate-preview-06-06` | Google – Imagen | API | Preview; matched as Imagen 4 Fast; $0.02/image | | `imagen-4.0-ultra-generate-preview-06-06` | Google – Imagen | API | Preview; matched as Imagen 4 Ultra; $0.06/image | | `imagen-3.0-generate-002` | Google – Imagen | API | Row matched via `imagen-3.0-generate`; $0.04/image | | `imagen-3.0-capability-001` | Google – Imagen | API – price not found | Editing/VQA feature model; no pricing row | | `imagen-3.0-capability-002` | Google – Imagen | API – price not found | Editing/VQA feature model; no pricing row | | `imagen-product-recontext-preview-06-30` | Google – Imagen | API | "Imagen Product Recontext"; $0.12/image | | `veo-2.0-generate-001` | Google – Veo | API | Row matched via `veo-2.0-generate`; $0.50/sec | | `veo-3.0-generate-001` | Google – Veo | API | Row matched as Veo 3 (video+audio rate); $0.40/sec | | `veo-3.0-fast-generate-001` | Google – Veo | API | Row matched as Veo 3 Fast; $0.15/sec | | `veo-3.0-generate-preview` | Google – Veo | API | Preview alias of Veo 3; $0.40/sec | | `veo-3.0-fast-generate-preview` | Google – Veo | API | Preview alias of Veo 3 Fast; $0.15/sec | | `veo-3.1-generate-001` | Google – Veo | API | Row matched as Veo 3.1; $0
View originalYes, Count offers a free tier. Pricing found: $0, $49, $69
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..
Based on user reviews and social mentions, the most common pain points are: token usage, ai agent, token cost, anthropic.
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Riley Brown
Host at AI Explained
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