Gamma

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Built an MCP server for options data so Claude can access gamma levels, flow, screeners, and signals directly in chat

I built an MCP server for my product, GammaHero, so you can connect it to Claude / other MCP clients and use options-market data directly inside your AI conversations. The main use case is pretty simple: instead of opening a bunch of tabs for option chains, screeners, levels, watchlists, and notes, you connect GammaHero once and then ask your AI for what you need in plain English. A few things it can pull into chat: ticker summaries with dealer gamma levels, put wall / call wall / hedge wall, IV rank, skew, GEX/DEX, options flow, implied move, momentum, etc. active trade signals like buy-the-dip, sell-the-top, resistance tests, plus conviction + key levels screener results for bullish / bearish candidates, support, resistance, long calls, long puts, volatility setups, high IV / low IV, etc. options distribution by strike or DTE your own GammaHero watchlist GOOGL analysis inside my Claude Chat So the workflow becomes more like: “Show me the gamma levels + active signal for TSLA” “What names are near strong support right now?” “Compare NVDA vs AMD from an options positioning perspective” “Which tickers in my watchlist have active signals today?” “Show me where gamma/open interest is concentrated for SPY by strike” What I like about MCP for this is that the AI is no longer guessing from stale web text or generic finance knowledge. It can actually use the same structured data that’s inside the product. One thing that feels underrated about MCP: this gets even more useful when you combine multiple finance MCPs in a single chat. Instead of one app trying to do everything, the AI can pull structured data from several specialized tools and reason across them in one place. I think there’s a big opportunity for SaaS products to build MCP servers around their best internal data/workflows. Setup is pretty quick in Claude (this is free at the moment, anyone can try it): Customize → Connectors → Add custom connector then paste: https://gammahero.com/ah-api/mcp/ I also support other MCP clients with an API key (which you have to generate on the settings page of my website). Would love feedback from anyone using AI for options, market structure, or trade idea generation. Happy to answer questions about the MCP implementation too. submitted by /u/CameraGlass6957 [link] [comments]

Claude AI Cheat Sheet

Most people use Claude like a chatbot. But Claude is actually a full AI workspace if you know how to use it. I broke the entire system down in this Claude AI Cheat Sheet: Claude Models Use the right model for the job. • Opus 4.5 → Hard reasoning, research, complex tasks • Sonnet 4.5 → Daily writing, analysis, editing (best default) • Haiku 4.5 → Fast, cheap tasks and quick prompts All models support 200K context, which means you can feed large documents and projects. Prompting Techniques The quality of your output depends on the structure of your prompt. Some of the most effective techniques: • Role playing • Chained instructions • Step-by-step prompting • Adding examples • Tree of thought reasoning • Style-based instructions The best combo usually is: Role + Examples + Step by Step. Role → Task → Format Framework One of the simplest ways to improve prompts. Example structure: Act as [Role] Perform [Task] Output in [Format] Example: Act as a marketing expert Create a content strategy Output in a table or bullet points Prompt Learning Methods Different prompt styles produce different outputs. • Open ended → broad exploration • Multiple choice → force clear decisions • Fill in the blank → structured responses • Comparative prompts → X vs Y analysis • Scenario prompts → role based thinking • Feedback prompts → review and improve content Prompt Templates You can dramatically improve results using structured prompting. Three core styles: • Zero shot → no examples • One shot → one example provided • Few shot → multiple examples More examples usually means better outputs. Projects Projects turn Claude into a knowledge workspace. You can: • Upload files as knowledge • Organize chats by topic • Add custom instructions • Share with teams • Maintain long context across work Artifacts Artifacts allow Claude to generate interactive outputs like: • Code • Documents • Visualizations • HTML or Markdown apps You can read, edit, and run them directly inside the chat. MCP + Connectors MCP (Model Context Protocol) connects Claude to external tools. Examples: • Google Drive • Gmail • Slack • GitHub • Figma • Asana • Databases This allows Claude to work with real data and workflows. Claude Code Claude can also act as a coding agent inside the terminal. It can: • Read entire codebases • Write and test code • Run commands • Integrate with Git • Deploy projects Reusable Skills + Hooks Claude supports reusable markdown instructions called Skills. Plus automation hooks like: • PreToolUse • PostToolUse • Stop • SubagentStop These help control workflows and outputs. Prompt Starters Some prompts work almost everywhere: • “Act as [role] and perform [task].” • “Explain this like I am 10” • “Compare X vs Y in a table.” • “Find problems in this document.” • “Create a step-by-step plan for [goal].” • “Summarize in 3 bullet points.” Study the cheat sheet once. Your prompting will immediately level up. submitted by /u/Longjumping_Fruit916 [link] [comments]

Prompt for generating images Claude

Note I can’t guarantee you’d be perfect or anything beyond 2D you will count to some issues this is a project at currently experimenting with Go ahead have fun. If possible, share some Discover or improvements with the community. # Claude Visual Generation Methods — A Complete Field Guide ## What This Document Is A reference for every method Claude can use to generate visual content inside artifacts, discovered through direct experimentation. Each method was tested, its ceiling found, its limits documented. This is the map of the territory. ----- ## Method 1: Pixel Art (Canvas Grid Rendering) **What it is:** Placing colored squares on a fixed grid — the same technique used in 8-bit and 16-bit game sprite creation. Each pixel is defined as a character in a string array, mapped to a color palette. **Best for:** Game sprites, retro-style characters, tile maps, icons, simple animations. **Resolution:** 16×16 to 64×64 is the sweet spot. Beyond that, the data becomes unwieldy. **Strengths:** - Extremely precise — every pixel is intentional - Sprite sheet animation (idle, walk, attack frames) is straightforward - Tiny file size, instant render - Scales cleanly with `image-rendering: pixelated` - The aesthetic *is* the constraint — chunky pixels are the point **Limitations:** - No smooth curves, no gradients within the grid - Detail ceiling is hard — a 32×32 face reads as “face” because the viewer’s brain fills gaps - Labor-intensive at higher resolutions (each pixel is a manual coordinate) **Animation capability:** Frame-based sprite sheets. Swap between pre-built frames on a timer. Smooth motion is an illusion of frame sequencing, not interpolation. **Color palette:** Best kept to 8–16 colors. Constraints force clarity. Dithering patterns can simulate additional tones. ----- ## Method 2: Canvas 2D Procedural Painting **What it is:** Using the HTML Canvas 2D API as a digital painting engine — bezier curves, radial/linear gradients, compositing blend modes, layered rendering passes. **Best for:** Character portraits, illustrated scenes, atmospheric environments, anything requiring painterly depth. **Resolution:** 800×1000+ at full detail. Limited only by computation time. **Strengths:** - Multi-pass rendering: background → character → foreground → post-processing - Gradient-based skin rendering simulates subsurface scattering - Variable-width bezier strokes replicate brush/ink pressure - Compositing modes (screen, multiply, soft-light) enable bloom, color grading, volumetric light - Perlin noise integration for organic textures (terrain, fabric, skin variation) - Film grain, vignette, bloom via downsampled buffer — proper post-processing stack - Breathing animation, hair sway, particle systems all run in real-time **Limitations:** - Every coordinate is hand-authored — no “happy accidents” - Faces plateau at “recognizable” rather than “expressive” — the millimeter-level asymmetry that makes a smirk read as knowing is extremely hard to nail mathematically - Curly/organic hair requires dedicated curl generators and still lacks the volumetric per-curl lighting of hand-painted illustration - Lines are mathematically smooth — they lack the confidence irregularities of a human hand **Ceiling we reached:** Multi-layer character portrait with strand-based hair, iris-fiber eye detail, subsurface skin warmth, layered forest environment with Perlin noise terrain, atmospheric mist, fireflies, volumetric moonlight, ACES tone mapping, and film grain. This was the highest fidelity static image achieved. **Key techniques discovered:** - **Strand-based hair:** Each lock is an independent bezier with its own gradient, width taper, and wind response - **Soft brush system:** `createRadialGradient` with transparent outer stop creates painterly soft dots - **Variable-width strokes:** Subdivide a bezier into segments, vary `lineWidth` per segment based on parametric t — mimics pen pressure - **Screen-blend rim lighting:** Draw highlight strokes with `globalCompositeOperation = 'screen'` for backlit edges - **Multiply color grading:** Full-canvas gradient fill with `multiply` blend shifts shadow tones warm or cool ----- ## Method 3: SVG Vector Illustration **What it is:** Mathematically defined vector shapes — paths, curves, gradients — rendered as scalable graphics. **Best for:** Clean illustration styles, logos, icons, diagrams, anything that needs to scale without quality loss. **Strengths:** - Resolution-independent — renders crisp at any zoom - Path data (`d` attribute) can describe complex organic curves - Built-in filter primitives (see Method 8) provide GPU-accelerated effects - Declarative structure — shapes described as markup rather than imperative draw calls **Limitations:** - Less control over per-pixel compositing than canvas - Complex illustrations produce large SVG markup - Animation is possible but less fluid than canvas `requestAnimationFrame` **Untapped potent

I built a Vibe Graphing orchestrator that chains Claude agents together

Been experimenting with something I'm calling Vibe Graphing — instead of writing agent pipelines in code, you just describe what you want and Claude designs the execution graph automatically.You review the graph, approve it, and it runs. Human-in-the-loop felt important — you see exactly what's going to happen before anything executes.Built on top of 5 MCP servers (scraping, memory, spec, logic-verifier, contracts). The orchestrator uses Claude Haiku to design the blueprints on the fly.Inspired by the MASFactory paper from BUPT-GAMMA — they showed that describing workflows in natural language instead of code reduced complexity dramatically. Wanted to see if it worked in practice. It does.Visualizer if you want to try it: https://mifactory-orchestrator.vercel.app/ui submitted by /u/No_Pressure7134 [link] [comments]

How to use Claude 4.6

https://preview.redd.it/1nin4ypm06og1.png?width=1006&format=png&auto=webp&s=0e31f116e96e44439005e2588087c98918e7ee6f just came across a nice post on Claude 4.6, how to use it. The actual post link is in the comment. You've spent hours prompting Claude. Nobody told you the prompt was never the point. Here's the actual setup nobody shows you: ▪️ Step 1. Pick the right model first Stop using one model for everything. → Sonnet 4.6 = your daily driver. Emails, summaries, spreadsheets, slides, coding. It's free. It's fast. It beats last year's Opus. → Opus 4.6 = your heavy lifter. Deep research. Complex code. 1M token context. It can read your entire company's codebase at once. Rule: If you're not sure which to use, start with Sonnet. Switch to Opus only when Sonnet falls short. ▪️ Step 2. Turn on Extended Thinking Most people skip this. Don't. → Open Claude. Start a new chat. → Click the model selector (top left). → Toggle "Extended Thinking" on. Without it: Claude gives you a surface answer. With it: Claude actually reasons through your problem. Yes, it's slower. It's worth it. ▪️ Step 3. Stop prompting. Start uploading files. There is no magic prompt. There is a magic file. → Open a Google Doc. → Write what you do, how you communicate, what you'd never say. → 80% of the file = what you'd NEVER write. → Save as .md format. → Upload it to Claude before every session. Your first prompt after uploading: "Read my files first. Ask me clarifying questions before you write anything." Claude stops guessing. It starts asking. ▪️ Step 4. Use Cowork (not just chat) Claude's chat window forgets everything. Cowork doesn't. → Go to claude.ai/download → Download the desktop app (Mac or Windows) → Open the Cowork tab → Click "Work in a folder" → Upload your entire project folder as a zip file Now Claude reads your actual files. Not what you describe. What you actually have. ▪️ Step 5. Connect your tools → Go to Settings → Connectors → Add: Slack. Google Drive. Notion. Gamma. Claude now reads your Slack messages. Pulls from your Google Docs mid-conversation. Builds slides directly from your Drive data. To build slides in one chat: "Make a 10-slide deck about [topic]. Pull relevant data from my Drive. Use my pre-saved Gamma style." No Canva. No PowerPoint. No exports. ▪️ Step 6. Use Claude in Excel → Open Excel → Insert → Get Add-ins → Search "Claude by Anthropic" → Install → Open any spreadsheet → Sign in Now prompt Claude inside Excel: "Build a 3-statement financial model. Monthly Year 1. Annual Years 2–5." "Add an Assumptions tab. Link everything." "Audit this model. Find broken references and hardcoded numbers." What used to cost $5,000. Now takes 12 minutes. Claude is not better because of the model. Claude is better because of what you feed it. submitted by /u/Forsaken-Reading377 [link] [comments]

I feel like a babysitter for Claude

I have to babysit Claude all the time, as it's extremely prone to jumping to conclusions. The current generation of LLMs does not seem to understand when it knows or does not know something at the level of a human. To take an example, I asked it today to construct a Bayesian layer for a plugin modeling a sequence of optimal actions. We want to learn true parameters of an underlying model in real time for each batch of data we receive. Anyways, I started telling it what I want, what are the inputs and expected outputs, and what is the expected architecture to do this. It started assuming a lot of things that were never mentioned, based on seeing what has been implemented and what's in the docs, and inferring what is the way to achieve this. For example, most of the parameters we want to learn is binary in nature, so it implemented beta conjugate for everything. Fine, I had to point it out when it generated the plan that there are cases where it's a point mass or inverse gamma. Then it assumed something else, and I had to correct it again. Then something else. To be clear I was not annoyed that it did not know what the correct approach is. Rather, what is annoying is that it does not ask and does not surface its assumptions made naturally, so I have to look at everything it does extremely carefully. So over the last month or so I started hunting down a meta solution to this annoyance. The first thing I tried was having system prompts or skills asking it to interrogate its assumptions for every planning decision it made. It did not work well, suffice to say, as it seems to generate assumptions based on what it has already written, rather than remember the assumption that it first made (if it even made any at all) while "reasoning" towards a decision. I then tried creating a ground truth-type system where there are many parts and assumptions in the codebase that we're making with various degrees of confidence, and instructed it to look it up whenever it is (i) uncertain about something (later (ii) whenever it was related). And if it does not find it in ground truth, it needs to ask me unless it's something trivial. (i) barely worked, as it can be considered as simply a mechanism merging the first approach + what I would've told Claude; in some ways it made it worse because now Claude thinks a ground truth entry means it can cease interrogating its own assumptions. (ii) made it work slightly better, but not good enough. That being said, I also do think that this problem would not be as huge if Anthropic didn't seem to have gone down a path of catering to pure vibecoders. There seems to be a trend towards making these models work as long as possible autonomously, instead of a collaborative approach where it asks a question the moment it's unsure of something. When looking at Claude Code I often (and very irritatedly) see Claude confidently asserting an assumption that seems to come out of nowhere. Things like "the database [always] tells us which parameters are relevant for a given model", and then drafts a plan on that assumption (that it's [always]), when the database only gives us relevant parameters for a subset of models. A more rigorous human would have found that the database is incomplete and does not have full coverage of all models, and therefore the things not covered by the database has to be routed through a different path in the code. A coworker would notice this and ask you what to do about it. Instead, half my Claude Code sessions is just witnessing assumption after assumption (some warranted, some unwarranted) and me having to stare at it and press Esc as fast as possible to stop it going down a wrong path. You'd then think that an interview approach would work well, where you let Claude interview you so that you can clarify any potential assumption that needs to be made. But this didn't work very well, because the fundamental problem I think is that LLMs do not have a sense of what is outside the text and context. It cannot imagine what is unsaid, as the universe of unsaid things for any given tasks is infinite. It has a very hard time inferring the most important unsaid things that it needs to care about based on what it sees or knows. As a result, the interview questions are rarely exhaustive and sometimes quite orthogonal to what really is the principal concern at stake. I also wonder if LLMs are trained on failures at all. A lot of important learning comes from failures. A human would quickly learn from their own baseless assumptions blowing up in their face what assumptions they must interrogate next time. I also tried telling it "the moment you make a confident assertion, stop and ask me", and it either seemed to do nothing, or it started asking me about stupid shit like whether something is uint or int (that it could check itself). Refining it to confident assertions vis a vis the actual logic or architecture did not seem to help either. The thing that did work the best, though unsa

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