308+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
Hi all, saw a few of these "what devs are actually doing with AI agents" threads floating around and wanted to compile a cleaner list before asking my own question, because most of the "what's your workflow" posts I see get buried under generic "I use it to write boilerplate" replies. These are 21 specific things people are doing with terminal coding agents (Claude Code, Codex,
A productive workday begins with intentional mental alignment before tasks take over your attention. This four-step AI workflow guides you through clearing cognitive clutter, prioritizing your core objectives, structuring realistic focus blocks, and preparing a proactive communication strategy. Mental Brain Dump and Priority Extraction This is the first step of your morning rou
Someone built a browser game where you play the human-in-the-loop for a coding agent: commands scroll past, you approve or deny under time pressure. About a third are attacks. After 40k+ sessions the average player had missed a third of the threats. And these were engaged players who knew they were being tested, with nothing else competing for attention. Your real setup has non
When I am mid-session on a work project, I will usually throw this boilerplate prompt to keep things going in the right direction. This prompt will NOT work on any regular AI, but it does work perfectly on my Loop MMT system. But I do think there are generally-applicable things here even still. *You have a lot of context left- you need to look back at what we have done so far i
Wanted to share a skill I built for designing and reviewing the prompts that run AI agents. Most prompt engineering advice for agents still treats the system prompt as a text block: "write a clear role, add examples, be specific." That helps with a chat answer, but agents fail in ways text-block advice doesn't cover. I kept watching the same three failures: an agent with overla
Most people run general Claude for everything and never touch these. They're free, official, and each one loads Claude with the workflows an actual specialist in that field uses. The legal one is the standout. Install it, then: Review the attached contract. Flag every clause that deviates from standard terms, classify each risk as low, medium, high or critical, and generate red
Been reading their cost-optimization guide and one sentence stuck: "a second model paid off in two shapes, an advisor and an orchestrator." Everything else they measured apparently didn't make the cut. Shape one: a low-cost model runs the whole loop and consults a frontier model only when stuck. Advice comes back short (400–700 tokens typically), you pay frontier rates only on
I use Claude and GPT daily and the thing I can't get past is the cadence. Not the vocabulary — the rhythm. Once you notice it you can't unsee it. The specific tics I'm trying to eliminate: Sentences built on "it's not X, it's Y" Em-dash asides everywhere Groups of three, constantly Setup-colon-payoff constructions Openers like "Here's the thing" or "Let's be clear" Every paragr
Seemed obvious. Agent kept re exploring the same sites so I cached what it found. Token spend dropped immediately. Then a site changed a form and the cached path kept running. Didn't error, didn't return empty, just returned the wrong field confidently for three days before I noticed. Moved to webcmd after that, which does the same explore-once-then-reuse thing but properly: co
Vibe-coded projects usually fail in two places. The code is coupled, so one change breaks three things you did not touch. And the UI has no states, so it looks fine until something is empty, slow, or wrong. Two old methodologies fix most of both. Axiomatic design (Nam Suh, 1990) for the first. Don Norman's design criteria for the second. Neither one is new and neither one is ab
Scheduled tasks used to require your machine on and the app open at that exact moment, which made them useless for anything real. That changed. Routines run on Anthropic's servers, so they fire whether your laptop is open, asleep, or in a bag at the airport. Where it is: desktop app, go to Code, then Routines on the left. Ignore the Code label, it's plain English instructions o
Recorded 15 seconds of myself talking normally, like telling a friend a quick story, quiet room, nothing special. Fed it in, and now anything I write can be read back in a voice that genuinely sounds like me, not a robot approximation. Runs through Claude Code, which is the version of Claude that can actually run commands rather than just chat. You point it at Fish Audio, a voi
Google DeepMind CEO and Nobel laureate Demis Hassabis sat down with Lex Fridman for an in-depth, 2.5-hour masterclass on the future of AI, world simulation models, and the architectural limits of LLMs. Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and engineering takeaways condensed into a 3-minute read: ⚡ Key Ta
If you've had AI build you a landing page you've seen the default: white background, Inter font, a purple-ish gradient, three cards. It's not your prompt. Left undefined, the model reaches for the average of everything it trained on, and that average is the generic template. Anthropic calls it distributional convergence and specifically flags Inter, Roboto, and purple gradients
I ran the whole set of 44 composable AI- and prompt-focused apps I just released on my website through my system and had it build out five real samples compositions that you can actually build today yourself, assuming you have the needed technical chops. Every one listed here uses only tools from the 44 and the system checked that the data actually flows; the port/typecheck/dec
Plain "summarize this PDF" prompts flatten everything to the same weight, so the one caveat that actually matters gets buried next to a throwaway line. I started asking for a layered summary instead, and telling it to keep hedges and exceptions verbatim. ``` Summarize the document below in three layers: One-line takeaway. 5-7 key points, most important first. Keep each to one s
Design background here. The request I get most is "can you make this into an infographic," attached to three paragraphs of dense text with no sense of what the one takeaway is. The tool is never the problem. The thinking that has to happen before the tool is the problem. So I wrote a prompt that does the structuring part, the part people skip. It doesn't design anything. It dec
Hey there, I just shared a project on GitHub: https://github.com/Shoko-official/Claude-Science-System-Prompts It contains system prompts, tool definitions, and skills used in the Claude Science workbench. Hope it's useful to some of you! Feel free to leave feedback or drop by! submitted by /u/Dry_Highlight7019 [link] [comments]
Claude does not make images or video by itself but you can hand it an api through a skill. I picked kie. ai for this because one key covers a bunch of models for both images and video and it runs on prepaid credits. Twenty five dollars got me five thousand credits with no subscription needed. Any rest endpoint should work the same but this one meant I did not need five separate
I am interested in understanding how people interact with an AI agent performing coding tasks for you. For example, for a bug fix, do you explain the bug, then iterate with the AI agent in the same thread until the bug is fixed, tested, and deployed? Or do you use separate chat threads for each stage of your development workflow? Similarly for new features, do you scope the fea
I’ve been testing out prompting using chatgpt and have run into this problem where chatgpt complicates a simple prompt. It’s giving me so much text that it’s burning through my tokens. How do I ask it to be more efficient, given that we’re in the middle of a project. I’m worried about messing up the quality of the prompts. Please lmk if y’all have faced this problem and how you
I worked with my system to create a suite of 44 free apps for AI work that you can find on my website. Hint- these tools are MAJORLY composable. You can have your AI system take a look at the collection to see how they can flow together and for suggestions on ways they can be used. Cairn — Keeps Git history alive across independent storage providers through priority-ordered clo
Wikipedia editors have spent months cataloguing submissions to figure out what gives away AI-generated text. They compiled a detailed community guide called Signs of AI Writing. I turned that guide into an open-source self-edit agent skill called Writ: 👉 https://github.com/Avinashricky211/writ What it catches: • Stock vocabulary: "delve", "tapestry", "testament", "seamless", "r
Andrej Karpathy (former Tesla Director of AI & OpenAI founding member) was recently on the Dwarkesh Podcast for a 2.5-hour deep dive into the engineering realities and architectural limits of modern AI. Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and core takeaways condensed into a 3-minute read: ⚡ Key Takeaway
Been noticing this at my job: everyone on my team is using AI daily now, but every prompt lives and dies in that one person's chat window. No sharing, no reuse, nothing. The part that bugs me most — I'll find out weeks later that a coworker independently wrote basically the same prompt I did for the same recurring task, just phrased slightly differently. We're both reinventing
so i noticed something weird last week was building a prompt to classify support tickets. bug report vs feature request. standard few-shot, gave it 3 clean examples of each. worked fine on my test data then threw a real ticket at it and it got it wrong. "the export button is too slow, we need this fixed" - it called that a feature request. which, fair, it kind of is. but the cu
MarkPad is the cleanest and simplest md editor I could possibly think of. I built it because every option I found was paid, not cross platform, too heavy, or it just showed me the raw syntax rather than the document. 3.3 MB. No account, no sync. Free and open source. Let me know what you think! ➡️ https://shiphrahx.github.io/MarkPad/ submitted by /u/-Shiphrah [link] [comments]
One of the easiest ways to improve AI outputs isn't writing longer prompts. It's giving examples. Instead of this: «Write a product description.» Try this: «Write a product description following this structure: - A short opening hook - Three benefit-focused bullet points - A professional but friendly tone - End with a clear call to action» Notice what's different. You're no l
Many accounts post essentially the exact same questions every few months. Im not kidding, many of these are a 1:1 per token match on wording, phrasing and sentence structure. Same wording. Same request for people to hand over their best prompt tricks. There was a previous post that received hundreds of upvotes and a large number of responses. Now they're doing it again. I obvio
Didn't expect anything, mostly did it out of boredom. Took a photo of myself I use everywhere, my Instagram profile pic basically, dropped it into google lens. Found it on three sites I've never heard of, one was some kind of profile aggregator with my name attached to it. Two minutes, no ai account needed for this part even, just: Go to images.google.com, click the camera icon
"Make me a presentation about X" gives you a generic table of contents every time. Intro, three vague sections, conclusion. The problem isn't the model, it's that "make slides" has no shape. Give it the shape and it gets a lot better. ``` Before writing any slides, build the spine of the talk. Step 1: State the ONE thing the audience should remember. Just one sentence. Step 2:
keep seeing people respond to a bad output by adding three more paragraphs of instructions. Most of the time the prompt was not too short. It was mixed. The pattern that fixed the most for me: physically separate the instruction from the material it operates on. When you paste a task and the content it applies to as one blob, the model has to guess where your command ends and y
If you use Claude Code regularly on medium or large projects, you have probably noticed how quickly a session can burn through API credits or hit rolling limits. Most usage trackers read local log files after the fact. That tells you the final token total for a session, but it does not explain how the request was assembled before the model ran. We set up local proxy capture to
Anthropic's latest technical insights for Claude Fable 5 highlight its remarkable capacity for nuanced storytelling, rich multi-character reasoning, and intricate instruction-following. However, getting Fable 5 to consistently sustain complex narrative worlds and strict logical constraints without drifting off-track requires a tailored structural framework. Digging through page
Analyzed a day of token logs across an autonomous coding agent setup running on internal codebases. The raw count: 769M input tokens vs 7.4M output tokens (~104:1). Because long agent runs re-read session history (files, AST diffs, test outputs) every turn, input costs accounted for ~95% of total spend. Optimizing output length turns out to be looking at the wrong variable. Thr
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