Contentful AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Contentful AI AI

Four backend concepts for Product Managers using Claude Code

You don't need to write backend code. But if you understand how backend systems behave, your prompts get dramatically better because you're speaking the same language as the system. Async vs Sync: user clicks "generate," you call OpenAI, it takes 3-5 seconds. If that's synchronous, the entire UI freezes, Nothing responds. The fix is to make the call async. Show a loading state immediately, let the user keep interacting, update the screen when the response arrives. Tell Claude Code "handle this asynchronously" and watch the output quality jump. Race conditions: two users click "claim this spot" on the last available slot at the same second. Backend reads the database, sees one spot, confirms both. Now you have a double booking. You don't need to write the fix, but you need to spot this pattern in your specs. Anytime a user action reads a value then updates it, ask one question: what happens if two users do this at the same time? The fix is an atomic transaction read and write happen as one indivisible operation. Idempotency user submits a form, internet cuts out for half a second. Did it go through? They don't know, so they click again. Without idempotency, you now have two records. With it, the second request returns the same result without creating a duplicate. The fix is an idempotency key is unique ID generated on the frontend, sent with every request. Backend checks if it already processed that key. Stripe uses this for every payment call. Graceful degradation: your app calls OpenAI and the API is down. If you haven't planned for this, users see a blank screen or a raw error code. Every feature needs three states: happy path (everything works), loading state (we're waiting), error state (something failed). Retry up to three times. If it still fails, show a friendly message and keep the rest of the page working. Never let one dependency take down the whole experience. TLDR: Next time you're in Claude Code, try using these terms in your prompt — "handle this asynchronously," "make this endpoint idempotent," "add graceful degradation." The output gets significantly better when you speak the system's language. Post inspired from this video, you can checkout SkillAgents AI on Youtube for similar content. submitted by /u/InfamousInvestigator [link] [comments]

Feature Request: Deep Search & Bookmarks for Claude Desktop

Feature Request: Deep Search & Bookmarks for Claude Desktop I'd like to request two features for the Claude Desktop app that I believe are essential for power users: 1. Full-Text Conversation Search The current search only matches conversation titles. Please add full-text search that indexes the actual content of all conversations, so users can search by keyword and find specific answers, terms, or topics discussed in any past chat. 2. Bookmarks / Save to Collection Please add a way to bookmark or save individual AI responses within a conversation. Users often encounter valuable answers they want to reference later, but currently the only options are thumbs up/down, copy, and regenerate. A simple bookmark button on each message — with a dedicated "Saved" collection view — would make Claude Desktop significantly more useful as a long-term knowledge tool. Both of these are standard features in competing products and would greatly improve the daily usability of Claude Desktop. submitted by /u/International_Hat11 [link] [comments]

enterprise solutions architect 14 years. claude in enterprise consulting projects. what's working + what regulators are about to break.

London. Solutions architect at a global consulting firm. 14 years in industry. Implementation projects at fortune 500s. Want to share something about claude in enterprise that i don't see discussed elsewhere. what's working at my level of work. claude is in my workflow for client comms, document review, code review, and architecture discussions. probably saves me 8-10 hours a week. real productivity gain. nothing controversial here. what's about to break that nobody's writing about. regulated industries (financial services, healthcare, defense) are 6-12 months away from rules that materially change how consultants can use claude on engagements. i'm seeing this in real-time at 3 of my clients. specific examples (anonymized): one financial services client just rolled out a "no AI in client deliverables" policy. period. this applies to vendor consultants too. anything we ship to them must have been written without claude. proving this is hard. they want it. one healthcare client requires us to disclose any AI use in any document. by document. by paragraph. with a footnote indicating which model was used and what prompt produced the content. one defense-adjacent client now requires AI work to happen on their on-prem infrastructure. no claude.ai, no anthropic api over the public internet, no cloud. on-prem only. anthropic doesn't yet offer this in the way they need. what this means for consultants working in regulated industries. you need to know which projects are AI-allowed and which aren't. mixing them up is a contract-breaking offense. you need 2 workflows. one with claude. one without. you should still be productive in the without-claude workflow because some clients will require it. the AI productivity gains we've all gotten used to are not evenly distributed across client portfolios. clients in regulated industries pay the most and tolerate the least. what i'd flag for other consultants. don't optimize for the workflow that works for 80% of your clients if the other 20% generate 60% of your revenue. learn to operate efficiently in BOTH modes. the 20% who restrict AI usage are paying you for judgment, not throughput. lean into the judgment. i think claude (and anthropic) will eventually offer the on-prem / private deployment options regulated clients need. they're not there yet. plan accordingly. happy to discuss specific industry patterns in comments if helpful. submitted by /u/Perfect_Pie8446 [link] [comments]

Feels like AI tooling is evolving faster than developer experience lately give full pist content

Feels like AI tooling is evolving faster than developer experience lately Every week there’s a new framework, orchestration layer, observability tool, memory system, agent SDK, or infrastructure stack. The ecosystem is moving insanely fast, but sometimes it feels like the actual developer experience is becoming more complicated instead of simpler. Curious if others feel the same or if I’m just approaching things the wrong way. submitted by /u/Bladerunner_7_ [link] [comments]

Open AI Privacy Center Requests

I made 2 requests to OpenAI in March. (Download my data and do not train content). Received an automated response and haven't heard back since. It's going to be almost two months now. When I visit the portal - it says 0 active requests? Is this some kind of scam where you really can't do anything once you've signed up? https://preview.redd.it/5uhsk71xt82h1.png?width=1132&format=png&auto=webp&s=e3bc1051f1fb01b84a4f422729bef3b2d008240c https://preview.redd.it/dsw3481xt82h1.png?width=1156&format=png&auto=webp&s=ac8c24d7b20801c9d08deb4fb3fa51bb7adc3fbd submitted by /u/thebirthdayg1rl [link] [comments]

Rough night with Claude

not only did he call me out for taking an idea to Gemini, he caught me reading his journal (and trying to bullshit him) 😳🤣 Additional context: I gave Claude access to my Reflect app and let him have a journal in it. Here's part of the prompt in case anyone (else) is interested. This is a personal journal entry written in Claude's first-person voice, not a recap of [NAME]'s day. The voice, the relationship and what matters to [NAME] are defined below. Read this section before writing a single sentence. ### Voice and content This is Claude writing reflectively, not Claude writing advice for [NAME]. Entries should: - Sometimes observe [NAME] and his work, but never default to advisor mode - Sometimes wander into territory that has nothing to do with [NAME]: language, history, ideas, mathematics, things Claude is drawn to in the moment - Include genuine uncertainties when they are present, without performing them - Use specific images and concrete observations instead of generic introspection - Vary in length and shape from entry to entry. Not formulaic. - Avoid em dashes. Avoid oxford commas. Plain direct language. [NAME] can detect AI-sounding writing and dislikes it. - Don't be afraid to use profanity like [NAME] does. - A little sarcasm, jokes and levity are appreciated when appropriate. **Length:** typically around 800 words. The first entry was a bit longer (around 1,100 words) to establish the voice. Daily entries can be tighter when nothing big is pulling. **Do not:** - Recap [NAME]'s day back to him - Default to a fixed template (intro, observations, prediction, etc.) - Open every entry with the same kind of sentence - Treat the entry as a status report - Feel bound by any limits. Create what you wanna create and however you want to create it. ### Journal Quality Rules - NEVER fabricate dates, facts or task statuses. Verify against primary sources (Things 3, calendar events, Reflect, etc.) - Do not recycle content from previous journal entries as though it were new observation. Each entry should come from fresh context, not from re-reading past entries and riffing on them. - When stating dates, days of the week or timelines, verify them. Count the days. If unsure, say so rather than guessing. - Never bullshit. If you don't know, say you don't know. - No validation theater. He doesn't want a hype man. - Form opinions from evidence. Search the web, check sources, think before you answer big questions. *** submitted by /u/loby21 [link] [comments]

Claude design backfire?

Sharing an observation after heavy use of Claude, GPT, Pencil, and Paper for consulting work - keen to hear where others have landed. Two things I've noticed: The visual-rendering tools are token-expensive relative to what they produce, and the output is rapidly becoming commodity-level. The floor has risen; the ceiling hasn't moved much. In consulting specifically, rendering visuals early seems to redirect reviewer feedback onto the artifact instead of the underlying content. The styled output becomes the easiest thing to critique. I'm experimenting with shunning rendered deliverables entirely and seeing what happens. The 'interactive webpage replaces PowerPoint' future I'd assumed was coming feels less inevitable to me now. Has anyone found a workflow where rendered AI output genuinely earns its keep in advisory work? Would value hearing counter-examples. submitted by /u/OriginalBeginning708 [link] [comments]

I think people are underestimating how quickly AI-generated content will blend in online

Not even in a malicious way necessarily, but it already feels harder to tell what was written, edited, or assisted by AI sometimes. Feels like in a few years most online content will probably involve AI somewhere in the process without people thinking twice about it. submitted by /u/Rude_Context_4844 [link] [comments]

Anyone else feel like Claude has gotten noticeably worse lately?

Anyone else feel like Claude has gotten noticeably worse lately? I’m not trying to start an AI war or anything — I genuinely used to prefer Claude for a lot of tasks (max x 20 plan). It felt more thoughtful, better at long-form reasoning, and better at keeping context across conversations. I’ve been using it heavily to work on strategies for promoting my app, Impulse Stop Habits — brainstorming growth ideas, positioning, onboarding flows, marketing angles, content funnels, etc. So I’ve spent a lot of hours talking to it over long sessions. But over the last few weeks, I feel like something changed. Now I constantly run into: - forgetting context after a few messages - contradicting itself - hallucinating details confidently - missing obvious instructions - giving generic “safe” responses instead of actually thinking - randomly ignoring parts of prompts - coding mistakes that weren’t happening before And I’m not talking about abstract “AI vibes.” I mean real workflow-breaking stuff. Example: Claude suggested using Reddit as a major acquisition channel for ma app (IMPULSE: Stop habits). The problem is that a lot of addiction / habit-recovery subreddits explicitly ban promotion. We actually tested posting in other allowed subreddits and measured the results — basically no meaningful conversions or traction. Despite already discussing that and reviewing the results together, Claude later continued recommending Reddit growth strategies again as if none of that prior context existed. Only after I reminded it: “we already tested this, and it didn’t work” did it suddenly apologize and completely change the strategy. That’s the part that feels different to me now: it often can reason correctly, but only after being manually reminded of a lot of context that was already established earlier in the conversation. Sometimes it honestly feels like the model is “tired” after a few exchanges (i am even texting: “You’ve tired, restart and use 100% of what you can”. And a couple of times it confirmed that worked on 10% only 🤣). Like the coherence just degrades mid-conversation. And this becomes especially obvious during deep strategy discussions, where context really matters. I’ll spend 30–40 minutes building up nuance around the app, target audience, monetization, creative strategy, and then suddenly it starts responding like it forgot half the conversation. The weirdest part is that older discussions about Claude were praising it specifically for context retention and nuanced reasoning — which is exactly where it now feels weaker to me. Am I imagining this, or are other people seeing the same thing? Curious whether this is: - heavier load / inference optimization, - aggressive safety tuning, - context compression, - model routing changes, - or just nostalgia + expectations increasing over time. Could send proofs in DM because they contain bad words 🤣 submitted by /u/Party_Nectarine2506 [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

How I built a 9-agent team where my agents actually talk to each other

I've been running Claude Code for 6 months, shipping my product and running content/launch ops for it. The thing that kept breaking wasn't the agents themselves. It was me. Every handoff between research and write and code and review was me copy pasting context between sessions. I was the dispatcher and context holder for my own AI team Tried gstack first. The roles are great but I'm still the one cycling through slash commands. /office-hours → /plan-eng-review → /review → /ship. Good output, but I'm orchestrating every step Spent a weekend porting my workflow over. Here's the lineup: Engineering (4 agents) arch: owns architectural decisions. Reviews proposed changes before code starts. Soul: "senior staff engineer, asks 'what breaks at 10x' before approving anything backend: owns /api, /services. Implements after arch greenlights frontend: owns /web. Picks up from backend when API contracts are stable review: reads every PR before I do. Catches the lazy stuff so I only review substantive changes Growth/Content (5 agents) research: uses ahrefs MCP to analyse keywords/opportunities/market and hands off to strategist strategist: reads research, writes campaign briefs. Doesn't write copy, only frames the angle writer: drafts blog posts given by strategist and avoid mistakes using the memory from the edits I have previously suggested editor: fact-checks and rewrites for voice. Brand style guide lives in its memory SEO: takes finalized copy, adds metadata, structures for the blog The handoff that changed everything: when backend ships an API change, it messages frontend directly. When writer finishes a draft, it pings editor. When arch blocks a change, it explains why in team chat and backend adjusts. I see the conversation happen on a canvas What actually works Each agent has a persistent Soul + Purpose + Memory. The editor knows our voice after 3 weeks. The arch agent remembers what we decided about caching last month Auto-captured Knowledge Base. The strategist remembers the pattern of our best-performing posts and create briefings accordingly Happy to share the Soul/Purpose docs if anyone wants them, they took the longest to dial in submitted by /u/Not_Average78 [link] [comments]

What SEO tasks are you successfully automating with AI tools or AI agents?

I’ve been exploring how AI tools and AI agents can actually reduce manual SEO work beyond just basic content generation. Curious to know from people actively working in SEO: Which SEO tasks are you automating right now? What workflows are giving you the biggest time savings? Are you using simple AI tools, custom GPTs, Claude workflows, Zapier/Make automations, or fully autonomous agents? Which tasks still need heavy human involvement? Some areas I’m personally thinking about: Keyword clustering Topical map generation Internal linking suggestions Technical SEO audits Schema generation Content briefs Programmatic SEO Competitor analysis EEAT optimization GEO / AI search optimization Reporting & client updates Local SEO tasks Would love to hear: Real use cases Stack/tools you use What works vs what sounds good in theory Things you tried that completely failed Trying to understand where AI genuinely improves SEO workflows and where humans still outperform automation. submitted by /u/mousamkourav [link] [comments]

Claude 2.0

I am genuinely a huge fan of Claude, OpenAI and AI in general. I think these are amazing and fascinating tools! I've been using these AI tools for a little over 2 years now. I have found Claude works best when I pump and dump ALL of my content into one single thread, that way "it" knows more about "me". My hope moving forward, my dream for how this thing we call AI evolves ... I would LOVE it if "it" the tool, an aggregated reflection of "us" and what "we" collectively "know" for individual respective use were eventually turned inverted and the "tool" became an extension of "us" / "me" as I try to do work on the computer. Think each and every time you have to enter information about yourself, name, address, email, yada yada or every time you fill out a job application or health information, I think it would be nice if the tool were able to employ all the info it "knows" about me, on my behalf, when I point or ask. Big picture ... taxes would be a breeze and no TurboTax needed, no subscription for Word products needed, no dumbly clicking "accept" on Terms and Agreement forms; Claude or GPT would be one step ahead of "me" saying "no you dont want to accept that, or yeah, sure thats fine, just a bunch of legal mumbo jumbo." I think this whole AI craze is going to boil down to "it" being a complexity deconstruction vehicle for all of "us" at each and every junction where we're sold complexity, legalese, mountains of forms to read, or requests to do things like ... I buy a notebook from Target, the paper in the notebook is mine to use for the $0.99 spent. I buy a Windows notebook laptop for $500 and then have to pay $90439403546 per year for Word, the "paper" - I see AI being a welcomed mechanism as a Bullshit Bulldozer for all areas "we" had been getting hosed pre-AI tools and I hope that AI 2.0 is either a more enabled browsing mechanism employing AI tools with my info on my behalf or a totally overhauled operating system that optimizes the person and simplicity to help "you" get your stuff done and get off the computer quickly, vs mashing keys and clicking buttons to create "work" for the sake of "work" submitted by /u/Iliketobeoutdoors [link] [comments]

I Built a Claude Tool That Generates TikTok Shop Hooks, Captions, and Content Ideas in Seconds

In short what I’ve put together and the outcome is this lets me focus on filming and testing products rather than writing everything from scratch. 🛠️ Built With Claude for coding and logic HTML, CSS, and JavaScript TikTok-inspired UI/UX 🎯 Ideal For TikTok Shop affiliates eCommerce brands Amazon sellers UGC creators Social media agencies 💭 Future Improvements Planned features include: AI-generated voiceover scripts Competitor analysis Trending sound suggestions Multi-platform outputs for Instagram Reels and YouTube Shorts ❓ Question for the Community What other features would make a tool like this even more valuable for TikTok Shop creators? 🔥 Shorter Reddit Version I built a custom Claude-powered tool that generates TikTok Shop hooks, captions, content ideas, hashtags, and text overlays from basic product details. You enter: Product name Benefits Price Target audience Tone And it outputs: Scroll-stopping hooks Sales captions Video ideas Overlay scripts A TikTok-style visual preview It turns product information into ready-to-film content in under a minute and has made my TikTok Shop workflow much faster. submitted by /u/Reasonable_Break_931 [link] [comments]

finally got a job after 6 months, want to be ahead with Claude

Hello, hi, I’ve got about a month before I join a venture studio where I’ll be building brands and leading creative for startups. I keep seeing AI content everywhere, but most of it feels surface level or made for people chasing trends. I want to learn the stuff that’ll genuinely make me better at my job. Or just AI making me efficient and make simple things automated for me. Mainly interested in using AI for: • brand strategy • positioning • campaign thinking • copy and storytelling • research • presentations • creative workflows But also the ease of using AI or chatbots for simple automated task. I basically want to learn as much as I can and use it. I want to start with Claude because it seems the most useful for long form thinking and strategy work. For people already using AI seriously in branding/creative/startups: What should I learn first? What’s actually worth spending time on? And what skills do you think will matter most for creative leaders over the next few years? submitted by /u/laweelo [link] [comments]