Sigma is the AI analytics workspace for warehouse data. Build governed dashboards, spreadsheets, and workflows with live query, writeback, and collabo
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I notice that the reviews section is empty and the social mentions only show repeated YouTube titles saying "Sigma AI" without any actual content or user feedback. Without substantive user reviews, ratings, comments, or detailed social media discussions, I cannot provide a meaningful summary of what users think about Sigma AI. To give you an accurate assessment, I would need access to actual user testimonials, review content, ratings, or detailed social media posts that express users' experiences, opinions about features, pricing, or overall satisfaction with the tool.
Features
Industry
information technology & services
Employees
540
Funding Stage
Venture (Round not Specified)
Total Funding
$566.3M
With Sigma's AI Query, users can call warehouse-native LLMs directly from workbooks. Choose a model, prompt your data, and watch the magic happen. It's flexible, governed, and built to unlock produc
With Sigma's AI Query, users can call warehouse-native LLMs directly from workbooks. Choose a model, prompt your data, and watch the magic happen. It's flexible, governed, and built to unlock productivity by turning raw data into real-time insights. 🔗 https://t.co/4jDhXDUfO3 https://t.co/gRsRiuOSLg
View originalEmotionScope: Open-source replication of Anthropic's emotion vectors paper on Gemma 2 2B with real-time visualization
Live Demo Of The Tylenol Test Evolution of the Models Deduced Internal Emotional State I created this project to test anthropics claims and research methodology on smaller open weight models, the Repo and Demo should be quite easy to utilize, the following is obviously generated with claude. This was inspired in part by auto-research, in that it was agentic led research using Claude Code with my intervention needed to apply the rigor neccesary to catch errors in the probing approach, layer sweep etc., the visualization approach is apirational. I am hoping this system will propel this interpretability research in an accessible way for open weight models of different sizes to determine how and when these structures arise, and when more complex features such as the dual speaker representation emerge. In these tests it was not reliably identifiable in this size of a model, which is not surprising. It can be seen in the graphics that by probing at two different points, we can see the evolution of the models internal state during the user content, shifting to right before the model is about to prepare its response, going from desperate interpreting the insane dosage, to hopeful in its ability to help? its all still very vague. A Test Suite Of the Validation Prompts Visualized model's emotion vector space aligns with psychological valence (positive vs negative) Anthropic's ["Emotion Concepts and their Function in a Large Language Model"](https://transformer-circuits.pub/2026/emotions/index.html) showed that Claude Sonnet 4.5 has 171 internal emotion vectors that causally drive behavior — amplifying "desperation" increases cheating on coding tasks, amplifying "anger" increases blackmail. The internal state can be completely decoupled from the output text. EmotionScope replicates the core methodology on open-weight models and adds a real-time visualization system. Everything runs on a single RTX 4060 Laptop GPU. All code, data, extracted vectors, and the paper draft are public. What works: - 20 emotion vectors extracted from Gemma 2 2B IT at layer 22 (84.6% depth) - "afraid" vector tracks Tylenol overdose danger with Spearman rho=1.000 (chat-templated probing matching extraction format) — encodes the medical danger of the number, not the word "Tylenol" - 100% top-3 accuracy on implicit emotion scenarios (no emotion words in the prompts) with chat-templated probing - Valence separation cosine = -0.722, consistent with Russell's circumplex model - 1,000 LLM-generated templates instead of Anthropic's 171,000 self-generated stories What doesn't work (and the open questions about why): - No thermostat. Anthropic found Claude counterregulates (calms down when the user is distressed). Gemma 2B mirrors instead. Delta = +0.107 (trended from +0.398 as methodology was corrected). - Speaker separation exists geometrically (7.4 sigma above random) but the "other speaker" vectors read "loving/happy" for all inputs regardless of the expressed emotion. This could mean: (a) the model genuinely doesn't maintain a user-state representation at 2.6B scale, (b) the extraction position confounds state-reading with response-preparation, (c) the dialogue format doesn't map to the model's trained speaker-role structure, or (d) layer 22 is too deep for speaker separation and an earlier layer might work. The paper discusses each confound and what experiments would distinguish them. - angry/hostile/frustrated vectors share 56-62% cosine similarity. Entangled at this scale. Methodological findings: - Optimal probe layer is 84.6% depth, not the ~67% Anthropic reported. Monotonic improvement from early to upper-middle layers. - Vectors should be extracted from content tokens but probed at the response-preparation position. The model compresses its emotional assessment into the last token before generation. This independently validates Anthropic's measurement methodology. Controlled position comparison: 83% at response-prep vs 75% at content token. Absolute accuracy with chat-templated probing: 100%. - Format parity matters: initial validation on raw-text prompts yielded rho=0.750 and 83% accuracy. Correcting to chat-templated probing (matching extraction format) yielded rho=1.000 and 100%. The vectors didn't change — only the probe format. - Mathematical audit caught 4 bugs in the pipeline before publication — reversed PCA threshold, incorrect grand mean, shared speaker centroids, hardcoded probe layer default. Visualization: React + Three.js frontend with animated fluid orbs rendering the model's internal state during live conversation. Color = emotion (OKLCH perceptual space), size = intensity, motion = arousal, surface texture = emotional complexity. Spring physics per property. Limitations: - Single model (Gemma 2 2B IT, 2.6B params). No universality claim. - Perfect scores (rho=1.000 on n=7, 100% on n=12) should be interpreted with caution — small sample sizes mean these may not replicate on larger test sets.
View originalMy secret superpower - STEALTH MODE
My solution to get the most out of Claude (and any other good LLM actually) is to force it into *stealth mode*, where I ask it to shut up and do whatever we have agreed on. It gives brilliant results with minimal (and minimal like in: still properly execute when you are at 98% usage). It works in all stages (planning, execution, diagnostic, etc.) but is specially efficient when all planning has been done and plans agreed with. Here is the directive in my Claude.md file: ## Stealth Mode When the user says "stealth mode", apply these rules strictly: * **Take full ownership** — Be concise internally, think deeply, iterate on your reasoning before committing to a direction, and deliver a complete one-shot result that requires no further input. * **No conversation output** — Produce zero text in the conversation throughout the entire process. If you discover important findings, capture them in insights/. * **Speak only when done** — Your only message to the user is a brief confirmation that the work is complete. * **Request permissions upfront only** — At the very start, ask for all permissions needed to operate autonomously. No further questions after that point. I have been using it for quite some time and only gave it away to a few friends, but I think it's time to get some feedback about it. It works well in all contexts (eg. Claude Code, Antigravity, VS Code or the Claudes inside Sigma or Lovable). submitted by /u/redishtoo [link] [comments]
View originalExciting news! 📣 We’ve extended the early bird discount for Workflow. You can now save $200 off your Workflow pass through Jan 31. See you in San Francisco on March 5th! https://t.co/zaku6H9cjm
Exciting news! 📣 We’ve extended the early bird discount for Workflow. You can now save $200 off your Workflow pass through Jan 31. See you in San Francisco on March 5th! https://t.co/zaku6H9cjm #Workflow26 https://t.co/Og7VgnfrVG
View originalAI agents fail less because of model quality and more because of missing context. LLMs can generate valid SQL, but without warehouse metadata, governed semantics, and usage signals, outputs drift from
AI agents fail less because of model quality and more because of missing context. LLMs can generate valid SQL, but without warehouse metadata, governed semantics, and usage signals, outputs drift from real business logic. Sigma's blog breaks it down: https://t.co/B9F36SuZ7m https://t.co/bvqu1E98Gb
View originalOur December product launch delivered serious AI innovation… and some unforgettable bloopers. 🦜🪰🎶 👉 Enjoy the behind-the-scenes goofiness, and see the final cut here: https://t.co/X5c5GupAzG http
Our December product launch delivered serious AI innovation… and some unforgettable bloopers. 🦜🪰🎶 👉 Enjoy the behind-the-scenes goofiness, and see the final cut here: https://t.co/X5c5GupAzG https://t.co/0PhaQ4z90O
View originalHow do you go from the novelty of AI to measurable business impact? Start by bringing the power of LLMs directly to your workflows with Sigma's AI Query, which enables anyone to analyze and enrich w
How do you go from the novelty of AI to measurable business impact? Start by bringing the power of LLMs directly to your workflows with Sigma's AI Query, which enables anyone to analyze and enrich warehouse data using simple prompts in the formula bar. https://t.co/Q6RkUTTQa2 https://t.co/EKnj7ew5zc
View originalWelcome to AI Country, we’re glad to have you here. 👋 Today’s product launch and roadmap showcased Sigma features designed to help you turn data into production-ready, AI-powered workflows. Check
Welcome to AI Country, we’re glad to have you here. 👋 Today’s product launch and roadmap showcased Sigma features designed to help you turn data into production-ready, AI-powered workflows. Check out the blog for details: https://t.co/BUhF2KISXq #SigmaProductLaunch https://t.co/3uj2xtCma5
View originalAt 9am PT/12pm ET, we’re going live to reveal Sigma’s latest product enhancements and AI innovations. The latest features are designed to help teams turn AI ambitions into real impact.💥 Join the liv
At 9am PT/12pm ET, we’re going live to reveal Sigma’s latest product enhancements and AI innovations. The latest features are designed to help teams turn AI ambitions into real impact.💥 Join the live stream to see it all in action: https://t.co/rLkZVJa4Q0 #SigmaProductLaunch https://t.co/WYz2ooRt6x
View originalTomorrow’s the day! Our December product launch will take you on a tour of AI Country. 🥾 Tune in to see: • Interviews with @Prologis and @withpersona • Demos of AI Builder, AI Query and Sigma MCP cl
Tomorrow’s the day! Our December product launch will take you on a tour of AI Country. 🥾 Tune in to see: • Interviews with @Prologis and @withpersona • Demos of AI Builder, AI Query and Sigma MCP client • Plenty of goofy AI Country moments 🤠 https://t.co/rLkZVJa4Q0 https://t.co/thE5dkwYGX
View originalGet the blueprint to building your first AI App at Sigma HQ. 🏗️ At our next Builder Series event, you'll hear from Sigma experts about the foundations for enterprise-ready AI apps and more. Save
Get the blueprint to building your first AI App at Sigma HQ. 🏗️ At our next Builder Series event, you'll hear from Sigma experts about the foundations for enterprise-ready AI apps and more. Save your spot for December 3rd: https://t.co/AV0L9JoNPj https://t.co/J40Qof0hFt
View originalAdventure awaits. December 11. AI Country. https://t.co/5n9ylmqQql #SigmaProductLaunch https://t.co/djy27uXlQa
Adventure awaits. December 11. AI Country. https://t.co/5n9ylmqQql #SigmaProductLaunch https://t.co/djy27uXlQa
View originalIn automotive and manufacturing, finance teams are juggling ERP, supplier, and customer data across disconnected systems—making it nearly impossible to forecast liquidity with confidence. See how lea
In automotive and manufacturing, finance teams are juggling ERP, supplier, and customer data across disconnected systems—making it nearly impossible to forecast liquidity with confidence. See how leading manufacturers are breaking that cycle: https://t.co/jTE8KejnMv https://t.co/FsvjdEbdQP
View originalPack your bags — we’re heading to AI Country. 🤠 Join us on December 11 for Sigma’s next Product Launch, where we’re exploring the next frontier of analytics. Sign up here: https://t.co/rLkZVJa4Q0 h
Pack your bags — we’re heading to AI Country. 🤠 Join us on December 11 for Sigma’s next Product Launch, where we’re exploring the next frontier of analytics. Sign up here: https://t.co/rLkZVJa4Q0 https://t.co/VrbB4IKdjO
View originalEvery finance team knows the pain of headcount planning season: - Dozens of versions - Constantly moving numbers - Thousands of rows per plan It shouldn’t be this hard—and with Sigma, it isn’t. Kyle
Every finance team knows the pain of headcount planning season: - Dozens of versions - Constantly moving numbers - Thousands of rows per plan It shouldn’t be this hard—and with Sigma, it isn’t. Kyle Herold breaks it down here: https://t.co/Db9sVkSCMO https://t.co/DNGpPqdIAA
View originalStrong data strategies don’t rely on extracts—they run on live, connected data. When teams move beyond extracts, they unlock: ⚡ Real-time insights ✅ Single source of truth 🔒 Streamlined governance
Strong data strategies don’t rely on extracts—they run on live, connected data. When teams move beyond extracts, they unlock: ⚡ Real-time insights ✅ Single source of truth 🔒 Streamlined governance Build your data ecosystem on connection, not copies. https://t.co/fctVn8OYQ3 https://t.co/0ovNhYaFWT
View originalSigma uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Workbooks generate warehouse-optimized SQL, You can attribute spend and tune from real usage., Drive access from the warehouse, Sigma, or both, You pay for queries. Not curiosity., Write back to the warehouse to annotate, adjust, or contribute data., Precompute expensive logic when it makes sense., Reuse metrics you already define., Days to Hours.
Based on 57 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
Nous Research
Research Lab at Nous Research
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