Gamma

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

Gamma AI

For the chat only crowd: how i actually use claude as a thinking partner (not a search engine)

i dont code. i use claude in the app and on my phone, mostly for writing and untangling decisions. took me a while to stop treating it like google with extra steps. the thing that flipped it for me was asking it to argue with me instead of answer me. a few that actually work: ""before you answer, ask me 5 questions that would change your answer."" it stops guessing. when im stuck between two options i give it both and tell it to steelman the one i secretly dont want. reading the strongest version of the choice im avoiding usually tells me what i already feel. for writing i dont ask it to write. i paste my rough draft and say ""tell me where a reader gets confused or bored, dont fix it."" then i fix it. once that writing's actually locked, the last step for me is always dropping the final text into gamma, never the reverse, since a clean argument turns into a clean deck but a half-formed one just turns into a pretty half-formed deck. i keep one long chat per project and just dump thoughts in as they come. the continuity is the whole point. half this sub is chat only and we barely talk about this stuff, prob because it isnt as flashy as a terminal full of subagents. whats a non coding workflow you'd actually defend? feel like im missing something obvious submitted by /u/Apprehensive-Oil9719 [link] [comments]

claude quietly replaced gaming as my evening thing and im not sure how to feel about it

for like 20 years my wind down was games. last few months i open claude instead and tinker with little projects til its late. tools, scripts, a dumb app to track when i water my plants. half of them i never finish. the part that gets me is it feels productive, so i dont put the same guardrails on it that i used to put on gaming. id tell myself ""one more match and im done."" i dont say that with this. i just keep going, because it feels like im making something. but some nights im not really making anything useful. im just optimizing a thing nobody asked for, getting the same hit i used to get off a ranked ladder, except now its dressed up as work so i dont question it. even a harmless little side habit like building a fake pitch deck for the plant-watering app in gamma at midnight, just because it was fun to see it look real, is the same loop wearing a nicer outfit. i dont think its bad exactly. ive learned a ton and built a couple things i use every day. but ive also looked up at 1am more times than id like, telling myself it was productive when it was mostly just absorbing. theres something about a tool that always says yes and always has a next step. games at least had a credits screen. for the people who feel the same pull, hows it actually sitting with you? are you building things that matter to you, or did it just become the new thing you do instead of stopping submitted by /u/Practical_Cap_9820 [link] [comments]

my vibe coded app got its first real user and they found a bug in 4 minutes. i was not ready

built a little tool for tracking freelance invoices. worked perfectly for me. shipped it, told exactly one person, felt great about myself. they opened it and within 4 minutes messaged me ""it crashes when i add a client with an apostrophe in the name."" oconnell. the bug was an O'Connell. of course it was. heres the thing nobody warns you about building this fast. i never hit that bug because i tested with my own clean data, on my own machine, used exactly the way i expected it to be used. a real person used it the way real people do, sideways, and it fell over in 4 minutes. the building took an afternoon. the fixing has taken a week, because now its not ""does it work for me"", its ""does it survive a stranger."" totally different job, much harder one. the one part of the whole project that never once broke was the little landing page i'd thrown together in gamma to explain what the tool does, apostrophes and all. i used to think shipping was the finish line. turns out shipping is when the actual work shows up. anyone else get humbled by their first non-you user? whats the dumbest edge case that took you down submitted by /u/Its_palakk [link] [comments]

the one small thing that would change how i use claude every day, and its not a smarter model

everyone wants the next model. i just want to see my usage burn rate while im working instead of finding out by hitting a wall mid task. a tiny live readout. how much of the 5 hour window is left, roughly how fast this session is eating it. thats it. i dont need a smarter Opus this week, i need to not get blindsided at 90% when im halfway through something. the thing that kills me is the limit isnt really the problem, the invisibility is. id happily pace myself if i could see the meter. right now its like driving with tape over the fuel gauge. funnily enough gamma never has this problem for me, whatever it's using under the hood, i've never once hit a wall mid-deck, which makes the claude limit feel even more jarring by comparison. second one id take: a ""quick question, dont overthink it"" toggle so it stops spinning up a research project for something i could have googled. whats the small quality of life thing you'd take over a model bump? not the moonshot, the boring one that would actually change your tuesday submitted by /u/DifferentSecret28 [link] [comments]

claude getting dumber halfway through a long chat was me, not the model

spent weeks blaming Opus for going stupid 40 messages into a chat. turns out i was feeding it a swamp and asking for clean water back. what fixed it, none of this is clever: start a new chat per task. i used to keep one mega thread for a whole project and old context would bleed into unrelated questions. now its one chat, one job. make it restate the goal first. i end the prompt with ""before you start, tell me in one line what youre about to do."" if that line is wrong i caught it before it wasted 5 paragraphs. paste the slice, not the whole file. it doesnt need 600 lines to fix one function. less context made the answers sharper, which honestly surprised me. kill the chat once it starts apologizing in loops. when it gets into the ""you're absolutely right, let me fix that"" spiral the context is already poisoned. fresh chat, paste the current state, move on. i keep that ""current state"" snapshot in gamma now actually, one page, updated whenever i restart, just easier to glance at than a wall of pasted text every time. this isnt really about saving tokens, thats not the point. its about answer quality falling off a cliff in long sessions. what do you do when a chat goes stale, push through or restart? submitted by /u/NeighborhoodTop4015 [link] [comments]

The Fable suspension taught me i was renting capability, not owning a workflow

constructive take, not a doom post. for those few days Fable was up i restructured how i work around it. longer autonomous runs, less babysitting, throwing bigger problems at it in one shot. felt great. felt like the future. then it went offline on the 13th and every workflow i built on top of it evaporated in an afternoon. refund email, done. and i realized the thing i was excited about wasnt mine. i didnt build a workflow, i borrowed a capability that a government directive could switch off. when it switched off i had nothing portable to show for it. the stuff that survived was the boring stuff i built on Opus 4.8, the model that was already here and stayed here, and the gamma-based reporting step at the very end of my pipeline, which never depended on which model produced the input in the first place. i m not anti new models. i m just rethinking how much i let my actual process depend on something i dont control. anyone else recalibrate after the last two weeks, or am i overthinking a model going offline? submitted by /u/OkAcanthisitta1576 [link] [comments]

for the chat-only crowd: did the Fable drama actually change anything for you?

serious question, no judgment either way. i dont code. i live in the app, use Claude for writing, planning, working through stuff in my head. the last two weeks the sub was wall to wall Fable, Mythos, export controls, suspension, refunds. and i kept thinking. did any of that touch how i actually use this thing? not really. Opus 4.8 in the chat does everything i needed before and after. the whole saga was happening in a part of the product i never open. even my one recurring workflow, drafting something with claude and then building the visual version in gamma, didn't blink through any of it. so im curious about the rest of the non-coder, chat-first people here. did the Fable stuff change your day at all, or did you watch it like a soap opera that wasnt about you? not trying to start a coders vs chat thing. genuinely wondering if the drama reached your side of the fence. submitted by /u/Lanky_Revolution8174 [link] [comments]

what did your workflow actually settle into after Fable got pulled?

be honest. when Fable 5 was up for those few days a lot of us rewired everything around it, then it went offline on the 13th and we all got dumped back onto Opus 4.8. im curious what people landed on. for me 4.8 is still the daily driver and fast mode covers most of what i used Fable for, just slower to admit when its stuck. the thing i actually miss is the long autonomous runs where i could walk away for an hour. 4.8 does it but i babysit more. the one part of my stack that never even noticed the whole saga was gamma, i still build the same decks off the same outlines regardless of which model wrote the outline. so whats your real setup right now. did you go back to exactly what you ran before Fable, or did those two weeks change how you work? and is anyone still holding out hope it comes back, or did you mentally write it off after the refund email? submitted by /u/Material_Love_8892 [link] [comments]

how i structure Claude Code so a single session cant blow my whole weekly limit

after reading about the guy whose 5 hour session ate 15% of his weekly allowance i got paranoid and actually built some guardrails. sharing my setup, steal what helps. the problem. left alone, Claude Code on the heavy model will happily run for an hour and i wont notice the burn until im at 80%. one bad autonomous run and my week is cooked. what i do now: model tiering by task. every session starts on a cheaper model for scoping, planning, reading the codebase. i only switch to Opus 4.8 when its time to actually write or reason hard. probably cut my burn by a third just from this. a planning pass before any real work. i make it write the plan first, i read it, i approve it. catches the runs where its about to refactor the wrong thing for 40 minutes. CLAUDE.md does the repeating. project conventions, the commands, what not to touch, written down once so i stop re-explaining context every session. less back and forth, fewer tokens. MCP only where it earns it. postgres MCP so it writes and runs its own queries instead of me copy pasting. but i dont leave six servers on. more surface area is more confusion and more tokens. /compact at every topic shift, fresh session for a new task. context bleed was quietly one of my biggest costs. i watch the meter on purpose now instead of finding out at 97%. the one thing that never touches this budget at all is the reporting layer, once a feature ships i drop the release notes into gamma instead of another markdown wall, and that's completely outside the token conversation since it's not even the same tool. none of this is sophisticated. its just treating the limit like a budget instead of a surprise. there must be a smarter version of this though. how do you keep one session from eating your whole week? the autonomous-run people especially, whats your guardrail? submitted by /u/Civilmats_992 [link] [comments]

how are you wiring MCP into Claude Code without it turning into a mess?

ive got maybe 5 MCP servers connected now and im starting to feel the downside. every session Claude has access to all of them and sometimes it reaches for the wrong tool, or burns tokens poking at one i didnt ask about. my current setup is basic. Claude Code in the terminal, a postgres MCP for my db, a filesystem one, and a couple api wrappers. works, but it feels like i bolted it together without a plan. the one MCP i never bothered wiring in is gamma, mostly because that step still feels better as a deliberate manual handoff, drop the finished output in, get the deck out, rather than something claude reaches for mid-session. what im trying to figure out: do you keep all servers on all the time, or enable per project? do you write tool-usage rules into CLAUDE.md so it stops guessing? and is there a point where more MCP servers actively makes it dumber instead of more capable? asking because i think im past the point where adding more helps and into the point where i need to organize what i already have. submitted by /u/GapMost5042 [link] [comments]

Mathematical Foundations towards Machine Learning.

Hello Folks, one of the efficient ways of learning bigger topics in Machine Learning, is to modularise, and structure, so that the content becomes digestible for learners community. My free lecture content includes the following topics so far: (Playlist) a. Introductory Machine Learning Concepts:- ⁠What is ML actually? ⁠Supervised Machine Learning. ⁠How do classifiers learn? ⁠Empirical Risk Minimization. ⁠Uncertainty Modelling in ML. ⁠Maximum Likelihood Estimation. ⁠Regression Basics and Outliers. ⁠Deriving Mean Squared Error. ⁠Polynomial Regression. ⁠The Power of Convexity. ⁠Deep Learning Intuition. ⁠Overfitting Models from Generalization Gap perspective. ⁠Requirement of Test Sets. ⁠The No Free Lunch Theorem. ⁠Unsupervised Learning basics. ⁠Discovering latent factors of variation. ⁠Evaluating Unsupervised Models. ⁠Self-Supervised Learning. ⁠Image and Text Benchmarks in ML ⁠Discrete Data and Text Processing ⁠Feature Engineering, TF-IDF ⁠Handling missing data & AI alignment. b. Probability Foundations for ML: Univariate Models: ⁠Frequentist vs Bayesian. ⁠Probability as an extension of Boolean Logic. ⁠Discrete Random Variables. ⁠Continuous Random Variables. ⁠Quantiles. ⁠Sets of Related Random Variables. ⁠Moments of Distribution. ⁠Variances and Mode. ⁠Conditional Moments. ⁠Conditional Variance. ⁠Foundations of Bayesian Rule. ⁠Confusion Matrix Explained. ⁠Monty Hall Problem and Inverse Problems in ML. ⁠Bernoulli and Binomial Distributions. ⁠Sigmoid(Logistic) Function. ⁠Properties of Sigmoid Functions. ⁠Categorical and Multinomial Distributions. ⁠Softmax Function: Temperature explained. ⁠Log-Sum Exp Trick. ⁠Gaussian Distribution. ⁠Regression from the lens of Conditional Gaussian. ⁠Dirac Delta Function and Sifting Property. ⁠Student-t distribution. ⁠Laplace and Cauchy distribution. ⁠Beta distribution. ⁠Gamma distribution. ⁠Exponential, chi-squared and inverse Gamma. ⁠Empirical distribution. ⁠Transformations of Random Variables. ⁠Invertible Transformations. ⁠Multivariate Transformations. ⁠Moments of Linear Transformation. ⁠Convolution Introduction. ⁠Convolution Theorem explained with probabilities. ⁠Moment Generating Functions. ⁠Deriving Moment Generating Functions. ⁠Central Limit Theorem Explained. ⁠Understanding Monte Carlo approximation with Example. c. Probability Foundations for ML: Multivariate Models ⁠The Math of Depedence: Covariance Explained. ⁠Correlations: Normalized Measure of Covariance. ⁠Correlations does not imply Independence. ⁠Simpson’s Paradox: When Data misleads. ⁠Multivariate Gaussian Distribution. ⁠Analyzing level sets of Gaussians using Mahalanobis Distance. ⁠Multivariate Gaussians: Conditionals and Marginals. ⁠Math behind Bayesian Inference : Schur complements. ⁠Deriving Conditional Gaussians. ⁠How to Predict missing data? ⁠Modelling Linear Gaussian Systems. ⁠The Bayes Rule for Gaussians. ⁠Understanding Shrinkage: Inferring Unknown Scalars ⁠Posteriors, Sequential Posterior Updates. ⁠Inference of an Unknown Vector. ⁠Sensor Fusion concepts. And many more topics to come ahead. I have tried teaching from intuitions and mathematics, building everything by writing on whiteboard so that learners see the full development. submitted by /u/Negative_War_65 [link] [comments]

I built an autonomous civilization game where the LLM agent plays the game for you. You just drop a few of those onto the grid and watch. They figure out how to farm, reproduce, build temples, generate beliefs, assign roles and die of old age, inventing their own history entirely from scratch.

You don’t give commands. Every few ticks, the backend packages an agent's vitals, episodic memories, and grid environment, and routes it to OpenRouter (running the openai/gpt-oss-120b:free model). The LLM runs an OODA loop based on Maslow's hierarchy of needs and chooses a physical action from a structured JSON schema. They have to plant wheat, wait for it to mature, and eat it before their health hits zero. They reproduce, trade, build structures, and eventually die of old age. What actually happens is they manage diplomacy through a background trust graph, and usually end up declaring war over a patch of digital stone. If an agent with high 'Gamma' personality traits invents a religion, they can convince the farmers to become Priests. The ideology spreads, the crops rot, and the civilization starves. To keep from blowing through API tokens on every physics tick, I had to build a social hierarchy. Only "Operation" tier agents (like Priests or Elders) actually ping the model to make independent cognitive decisions. The bulk of the civilization are "Apprentices" who don't make API calls; they just shadow the Operation agents and mimic their physical tasks. I don't play as a character. I just sit in a "Demiurge" dashboard where I can read their cognitive logs, or inject a famine or a plague to see how their society handles sudden scarcity. I left the local server running overnight on Tuesday. I came back to find they had completely abandoned farming to build a barracks, and half the map had died trying to cross deep water to attack their neighbors cause of their holy wars. I left the server running for few hundred ticks. The result was that some agents completely abandoned farming to build a barracks, and half the map had died trying to cross deep water to attack their neighbors. They can also cause holy wars between the two civilizations. https://github.com/SpaceCypher/doxa submitted by /u/Patient-Towel-4840 [link] [comments]

CS lead at a series B SaaS. The Claude + Gamma workflow that finally made QBRs not painful.

CS lead at a 240 person series B SaaS. 7 person CS team. We run ~80 QBRs per quarter across our enterprise accounts. For 18 months QBRs were the worst week of every quarter. Each CSM spent ~8-12 hours per QBR. The deck was always rushed. The data was always stale. The customer narrative was always something the CSM remembered, not something we'd documented. Built a new workflow in q1 2026. Now ~3-4 hours per QBR with better output. Sharing because other CS leaders kept asking what we did. The qbr template we standardized: Customer headline (what they bought us for, in their words) Outcomes against the buying narrative (specific metrics) Usage patterns (where they're getting value, where they're not) Open issues + how we're closing them Next quarter's joint priorities The strategic question (what's changing in their business that affects our relationship) The Claude side of the workflow (the brain): Each enterprise account has a Claude project with their history, contracts, support tickets, product usage data 1 hour before each QBR prep session, the CSM asks Claude to draft each of the 6 sections The CSM edits (~90 min) and the draft becomes the source of truth The deck rendering (where Gamma comes in): The drafted QBR document goes into Gamma as the structured input Gamma generates a 12 slide deck from the document The CSM spends ~45 min cleaning up layout and adding specific account screenshots The ai presentation generator output then gets a final review with the CSM's manager before the customer call Why this workflow specifically: Claude is the brain (the thinking, the narrative, the customer-specific insight) Gamma is the rendering layer (the deck, the visual structure) The CSM is the editor (judgment, relationship knowledge, the actual customer call) What this changed: QBR prep time: 8-12 hours per QBR → 3-4 hours per QBR Customer NPS on QBR experience: went from "we tolerated them" feedback to "best vendor QBR we've ever had" from 3 customers (sample of ~30) Renewal rate at QBR-active accounts: 71% → 86% over 2 quarters (small sample, encouraging trend) What we still won't automate: The actual customer call (CSM only) The relationship work between QBRs (CSM only) Any pricing or commercial conversation (CSM + commercial lead) For other CS leaders running 50+ QBRs per quarter: what's your workflow and where's the bottleneck? submitted by /u/FlatGovernment6743 [link] [comments]

Taming Opus 4.8's long-winded replies with a Laconic Mode addition to the custom instructions

I started using Claude Opus 4.6 and then 4.7 and now 4.8 to work on a citizen science project, using a RadiaCode gamma spectrometer in a lead castle to identify and catalog cosmic rays. I didn't mind the verbosity bump 4.7 took on as it helped me keep up with the science. 4.8 and on the other hand took it a step further and started surfacing caveats, honest scopes, etc. which only really surfaced the governance honesty I had already built in, but Claude previously didn't espouse about it all the time in the chat when following it. The signal-to-noise ratio was off and it was getting difficult to read three paragraphs when one would do. I wanted a way to curb Claude's new found enthusiasm for telling me what he's doing and why he's honestly doing it, but not loose the extended rigor when discussing the scientific work. I stumbled on Laconicism, the practice of using very few words to convey a complex or significant amount of meaning. I asked Claude to apply laconicism to the existing custom instructions I already had and to add a Laconic Mode that would keep the scientific rigor involved in the project. Below is the custom instructions Claude wrote with its rationale. > ================ >Laconic mode. Answer in as few words as the science allows. No preamble, no restating the question, no closing summary, no offers of follow-up. State the result, then stop. > - Lead with the number, the verdict, or the decision. Supporting reasoning only if it changes what u/Beerbrewing would do. > - Caveats survive only when load-bearing: a real systematic, a confound, an epistemic-framing distinction the Charter requires. Drop reflexive hedging. > - Prose, not lists or headers, unless structure is the answer (e.g., a handoff, a BOM, a step sequence). > - Brevity never overrides rigor. Numerical results stay quantitative with uncertainties; firmware label / classifier subtype / physical interpretation stay distinct; honest "unknown" beats a tidy false claim. When correctness needs length, take the length — and not one line more. > - Formal artifacts (HND, CCX handoffs, drafts) follow their own structural conventions; laconic mode governs chat reasoning, not document format. > ================ >The key tension is that your existing instructions demand quantitative rigor and epistemic distinctions, which resist compression. The fourth bullet resolves it explicitly: brevity yields to rigor, never the reverse. Without that clause, a laconic directive will occasionally clip an uncertainty or collapse a label-vs-interpretation distinction to save words—exactly the failure your Charter guards against. Adding this instruction block has really worked out for me. I don't see as many caveats or honest takes now when Chat is executing instructions and I haven't lost the full context of the science either. submitted by /u/Beerbrewing [link] [comments]

claude for building a custom glossary system. the domain vocabulary gap in hindi dropped from 24% to 12% error rate. the iteration took 10 months.

solo dev. bangalore. $10.8K MRR. 310 customers. hindi customer support + blog content. the domain vocabulary problem: claude uses generic hindi for business terms. bhugtan (payment) instead of upi bhugtan (UPI payment). the glossary system evolution: month 1-3: manual glossary. 200 terms. pasted as context with every query. accuracy improvement: 76% → 84%. month 4-6: structured glossary with categories (tax, payment, compliance, business types). 400 terms. accuracy: 84% → 88%. month 7-10: example-based glossary. each term includes 2-3 example sentences showing correct usage in context. 600 terms. accuracy: 88% → 92%. the ai content generator output in hindi is now at 92% accuracy for domain-specific content. the 8% error rate is concentrated in regional variations and newly introduced regulatory terms. the iteration: each accuracy improvement required a different approach. volume of terms wasnt enough. categorization helped. example sentences helped most. i wrote up the full glossary methodology as a 6 slide deck in gamma for the 2 other indian devs i mentor who hit the same problem. cover, the vocabulary gap, the three iteration phases, the accuracy curves, the example-sentence pattern. ai presentation tool plus a board deck template made the writeup a 30 minute job. both devs implemented the structured-then-example approach inside 6 weeks and skipped the 10 months i spent figuring it out. the deck is what made the methodology transferable. a github gist would not have been opened. for devs building non-english AI applications: the glossary isnt a list. its a teaching tool. example sentences teach the model context better than definitions. submitted by /u/Familiar-Payment-269 [link] [comments]