BuildShip powers businesses to visually create AI workflows using natural language. Automate complex backend, develop tools for your AI agents, and ea
Users praise BuildShip for its intuitive interface and robust project management capabilities, which streamline app development processes. However, some users express frustration with occasional bugs and perceived slow customer support response times. The pricing is generally considered fair, but a few users feel the cost could be more competitive given its feature set. Overall, BuildShip holds a good reputation for enhancing app development efficiency, although some expect further improvements in reliability and support.
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Users praise BuildShip for its intuitive interface and robust project management capabilities, which streamline app development processes. However, some users express frustration with occasional bugs and perceived slow customer support response times. The pricing is generally considered fair, but a few users feel the cost could be more competitive given its feature set. Overall, BuildShip holds a good reputation for enhancing app development efficiency, although some expect further improvements in reliability and support.
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I'm a software engineer with a decade of experience. This is how I'd approach learning to build apps using Claude Code if I were starting from scratch today:
I'm going to describe a person this post is for, if this is you, I think I can be of some assistance: * you are new to coding * you are blown away by how it unlocks this magical ability that was previously inaccessible without years of training and effort * you've daydreamed of business and app ideas but never knew where to start before or how to build them * you've been vibe coding non-stop and burning through tokens * you're unsure about what's secure, how to structure the systems, and how systems are supposed to interact with each other. So, essentially the plumbing separate from the code itself: hosting, authentication, APIs, version control, testing, analytics, etc If any of this resonates with you, I think I can help! Now disclaimer: I'm *not* a pro at creating startups, acquiring users, marketing or any of that kind of stuff. Where I do have tons of professional experience is with the last bullet point above. And now onto it! This might be controversial, but if I were in your position I would *not* start with the code, the lowest level. In fact, I would do the opposite and start at the **highest level**. What does that mean? I'd argue that for people starting today, the most important thing is learning about the fundamentals of what makes a solid application at a high level. The system architecture. That's what I'll be covering for the rest of the post. What are the building blocks of a secure, full stack software application. There's so much to this that I'll stay high level for this one and go with breadth. If people are interested, I can (and honestly would love to) make dedicated posts on each of the topics I list below. So what is the main architecture for a software application? There are four main components and lots of specifics below each. 1. Front end -> this is what the user sees. The website, the mobile app, etc 2. Back end -> the main logic and rules of the app 3. Database -> where the data lives 4. The plumbing -> how everything connects and stays standing Of all of these, I could talk for hours, so to keep things brief, I think I'll focus on the highest impact and the biggest gap which is 4. The plumbing. Why? If you asked Claude, or whatever agent you use, to setup a front end, back end, and database it could do it quite easily. In fact, I'd imagine for apps you've vibe coded, it already has! There is tons to cover with the first three topics, but I think the plumbing is the area where getting some seasoned tips would help the most. # The Plumbing -> how everything connects and stays standing Here's where it gets real. When you vibe code something and it runs, it feels done. It looks done. But what you're looking at is the tip of the iceberg, the part above the water. The plumbing is everything below the waterline that nobody sees, but that decides whether your app is a weekend toy or something real people can actually trust with their data and their money. (It's also the part the AI will happily skip unless you know to ask for it. So this is the stuff worth knowing by name) I've grouped it into four questions. If you can answer these about your app, you're already ahead of most vibe coders shipping today. # How does everything talk to each other? Your frontend, backend, and database aren't one blob. They're separate pieces passing messages back and forth constantly. This is the part that's invisible but always running. At a high level, for most applications this is done via: * **APIs**: the set of "doors" your frontend uses to ask the backend for things ("give me this user's orders"). There are other ways, but this is the one you should probably focus on at first. # Where does it live, and how does it get online? Right now your app probably only exists on your laptop. Getting it onto the internet, and keeping it there, is its own thing. * **Hosting**: where your app actually runs so the world can reach it. This is where servers come into play. * **Domains & DNS**: your custom address (yourapp.com) and how it points to your servers. * **Deployment**: the pipeline that takes the code you wrote and safely publishes it for your users to see. * **Environment variables & secrets**: where you stash your passwords and API keys so they're not sitting in your code for the whole world to copy. People get burned by this constantly. # Who's allowed in, and is it safe? This is the one I'd beg you not to skip. The magic of vibe coding makes it dangerously easy to ship something insecure without realizing it. But don't fear! There are existing ways to do this (and not from scratch). * **Authentication**: how your app knows who someone is. The login. * **Authorization**: what someone's allowed to do once they're in. The difference between a normal user and an admin who can delete everything. * **Security**: the broad practice of not leaving doors unlocked. This one is the hardest because you can have security issues at every level of your stack. It's defin
View originalPricing found: $0 /month, $0 /year, $19 /month, $225 /year, $29 /mo
I've built AI agents for dozens of clients. Here's why most of them fail in production (and it's not the model)
I see a lot of people shipping AI agents that work perfectly in demos and fall apart the moment a real user touches them. After building automation systems for multiple clients, I've noticed the failures almost never come from choosing the wrong LLM. They come from three things: 1. Bad chunking in RAG pipelines. Everyone's so focused on picking the right vector DB that they don't think about how they're splitting documents. Garbage in, garbage out. If your chunks don't preserve context across sentences, your retrieval will always be mediocre. 2. Prompts written for demos, not edge cases. Demo inputs are clean. Real user inputs are weird, vague, and sometimes intentionally broken. If you didn't stress test your prompt with bad inputs, it will fail publicly. 3. No fallback logic. When the agent is confused, what does it do? Most builders never answer this question. So the agent either hallucinates confidently or returns nothing. Both are bad. The model is usually the last thing to blame. Fix the scaffolding first. Anyone else running into this? Curious what failure patterns you've seen. submitted by /u/ahmadparizaad [link] [comments]
View originalCognitive debt might be the most underrated problem AI is creating
Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [comments]
View originalbuilt a small open source tool to stop AI agents from regressing after changes
one of the most annoying problems when building AI agents: fix a failure, change something, same failure comes back quietly. built replayd for this. captures failed runs as regression tests and replays them before you ship. catches the failure if it returns after a prompt, model, or tool change. v0.1.2, pip installable, open source. pip install replayd star it if you want to follow progress. submitted by /u/taimoorkhan10 [link] [comments]
View originalWeekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel
Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]
View originalI built a Claude/Codex skill that researches comparable repos before giving project advice
The annoying thing I kept seeing: AI tools recommend stacks with full confidence, even when they haven’t checked what similar projects actually used. So I made advise-project-approach. It supports three moments: before building, when you’re choosing the stack mid-build, when the project is getting messy after building, when you want a review before shipping The skill looks for comparable real-world repos first, then gives stack direction, architecture notes, alternatives, build/improvement plans, and where the recommendation might break. Repo: https://github.com/AaravKashyap12/advise-project-approach I’d genuinely like feedback on the SKILL.md itself. Is the workflow too strict, too broad, or actually useful? submitted by /u/Scared_Objective_345 [link] [comments]
View originalFrom "AI as autocomplete" to "AI as cognitive infrastructure" ... my Claude build process
Crossposting context: shorter version of this went up in [r/ClaudeCowork](r/ClaudeCowork) earlier today for that audience. Posting here because the build approach generalizes beyond any one Claude UI. Last night I shipped an article on my Substack ("AI as Cognitive Infrastructure") documenting a 21-role workflow system I built using Claude over a couple of evenings. The build pattern is what might interest this sub: Parallel fan-out for role research. Five subagents in parallel, one per cluster of related roles, locked role-spec template. Twenty-one grounded specs in under thirty minutes of clock time. Sequential would have been weeks. Discipline grounding, not generic AI advice. Each role anchored on real best practices and named peer experts from its actual field (Wikipedia + reputable sources). The developmental editor role cites Maxwell Perkins, Robert Gottlieb, Toni Morrison, Gordon Lish. The coach role cites Russell Barkley on ADHD executive function. Not vibes-based expertise. Cited expertise. Gating bars per role. Explicit propose-vs-act-vs-never-without-approval rules. Counters the AI-drifts-into-co-authorship failure mode. Scheduled-task recurring cadences. Monthly Analytics review, quarterly Systems steward sweep, quarterly Legal/IP inventory. The system fires itself; I don't have to remember to invoke. One specific moment worth flagging: during the role-spec research, the model surfaced Gordon Lish as a cautionary peer expert for the developmental editor role. I didn't know who Lish was when I started. Verified the Carver story, pulled it forward into the article. That's the substrate doing what it's supposed to do...surface expertise I don't have, let me validate and use it. Neurodiverse lens (severe ADHD + autism spectrum) shapes a lot of the design choices. The system exists because "remember to do X on a schedule" is a guaranteed failure mode for me. Happy to talk through any of this. Article: https://jeffmaaks.substack.com/p/ai-as-cognitive-infrastructure submitted by /u/jmaaks [link] [comments]
View originalMy experience with Second brain using Obsidian and Claude, and step by step guide
Hey, I heard a time ago about the second brain approach: you have a memory, and using AI to manage it, will help you to sturcture your thinking. I started playing with it 3 months ago, and i would say it was a nice experience, but it was alaways getting a mess, and break. Each time i was learning from the community , and from other places. I did the last version 3 weeks ago, and so far, it is staying. I want to share this with the community so they can replicate it. TBH, i love having this second brain, I m using it for my personal and proffessional life, and i would recommend anyone to do that This is how I set it up Plain markdown in Obsidian (PARA folders plus a 00-Meta folder and a 05-Daily folder) A CLAUDE.md in the meta folder that Claude reads first every session: who I am, what I'm shipping, decisions that are locked A memory directory, one file per fact (decision_pricing_locked.md, etc.), so it stops asking what I already decided Slash commands in .claude/commands/. The four I run daily: /context (loads the vault state), /today (a briefing), /log (turns an evening voice memo into a structured note), /sunday (reads the week, returns one win, one friction, one change) The detail I didn't expect to matter: the wikilinks aren't for the graph view, they're so Claude can hop from a project file to a linked decision note on its own. I wrote up the full build and turned the scaffold into a prompt you paste into Claude that generates the whole vault. Free download, mine, no catch: https://choumed.gumroad.com/l/nhgsxf Any feedbacks or any one had experience about second brain? for which workflow are you using it exactly? Ps: the original post was at /claudeCode subrredit submitted by /u/MaterialAppearance21 [link] [comments]
View originalSpent 1,156,308,524 input tokens in May 🫣 Sharing what I learned
After burning through 1.15 billion tokens in past months, I've learned a thing or two about the tokens, what are they, how they are calculated and how to not overspend them. https://preview.redd.it/rurt4skju14h1.png?width=2432&format=png&auto=webp&s=b5f1d8b743bc23e14bc8854d71c8490bab73c819 Sharing some insight here below. What the hell is a token anyway? Think of tokens like LEGO pieces for language. Each piece can be a word, part of a word, punctuation, or a space. Quick examples: "OpenAI" = 1 token "OpenAI's" = 2 tokens (the apostrophe-s gets its own) "Cómo estás" = 5 tokens (non-English languages tokenize worse) https://preview.redd.it/9xzakaiwv14h1.png?width=1080&format=png&auto=webp&s=5d726a0258c36baa68ad6d130f495172a52425d9 Rule of thumb: 1 token ≈ 4 characters in English 100 tokens ≈ 75 words Use Claude tokenizer to check your prompts. One thing most people miss: JSON is a token pig. Brackets, quotes, colons, and commas each consume tokens — a compact JSON object uses roughly 2x the tokens of equivalent plain text. If you're sending structured data as context, plain text or markdown tables are significantly cheaper. How to not overspend — the full list 1. Choose the right model (yes, still obvious, still ignored) Current Claude pricing (per million tokens): Haiku 4.5 at $1/$5, Sonnet 4.6 at $3/$15, Opus 4.6 at $5/$25. Batch processing is 50% cheaper across all models (you might need to wait up to 24h to get results, usually they come back in 2-3h). https://platform.claude.com/docs/en/build-with-claude/batch-processing For comparison, if you're on OpenAI, the spread between mini and o1 is even more extreme. Most tasks don't need your flagship model. Audit your model usage frequently, models that were too weak 6 months ago might now be good enough.... If you want a single interface across OpenAI, Claude, DeepSeek, and Gemini, OpenRouter is worth it imo. 2. Prompt caching For Claude, prompt caching cuts cached input cost by 90%. Still the single highest-ROI optimization if you have long system prompts. The rule is still: put dynamic content at the end of your prompt. But here's what changed: Anthropic quietly changed the prompt cache TTL from 60 minutes down to 5 minutes in early 2026. For many production workloads, this single change increased effective costs by 30–60%. If you haven't audited your cache hit rates recently, do it now here: https://platform.claude.com/usage/cache https://preview.redd.it/ongee5v3w14h1.png?width=1080&format=png&auto=webp&s=fefe5d0093be0a26894fe0ddd9d92e1283b02572 3. Minimize output tokens!! Output tokens are 5x the price of input tokens. Instead of asking for full text responses, have the model return just IDs, categories, or position numbers... and do the mapping in your code. This cut our output costs ~60%. 4. Be careful with new model versions Opus 4.7 ships with a new tokenizer that can generate up to 35% more tokens for the same input text compared to Opus 4.6. 5. Set up billing alerts I cannot stress this enough. Set a hard budget cap and tiered alerts (50%, 80%, 100%). One runaway loop once cost me more than a week of normal spend in a single night. Hopefully this helps! Tilen, founder of AI agent that automates SEO/GEO (we consume a lot of tokens) 😄 submitted by /u/tiln7 [link] [comments]
View originalLoom for Claude
Yo! Solo founder, built this to help myself while working on my main startup. Turned out to be pretty useful so I thought I'd wrap it up for others to use. The problem: I use Cursor and Claude Code daily. The slow part isn't typing prompts anymore (Wispr Flow + voice mode already solved that) — it's explaining which screenshot goes with which sentence. "The button on the right of the second screenshot, the orange one, no, that one..." Dis Dat: press ⌃⌥⌘Space, talk while pointing your cursor at things, press again. A link lands on your clipboard. Paste it into Cursor, Claude Code, Codex, Lovable, v0... The agent goes and fetches your feedback — what you were saying, where you pointed — and ships the changes. Free to try, $19/mo for unlimited. Works with any AI vibe coding soon. Mac only for now (Apple Silicon + Intel). Also building a mobile version. open any page on your phone, talk as you scroll, and the link lands on your Mac ready to paste. So you can react out loud to your own product without sitting at your desk. Coming soon; happy to share more if anyone's curious. Things I'd genuinely value feedback on: What's the workflow you'd want this to slot into that I'm missing? What other agents would you want this to work with first? Anyone tried something similar and bounced off it... what killed it? I'll be here all day. Roast away. submitted by /u/Emergency_Bar_428 [link] [comments]
View originalIs this tagline intentional?
submitted by /u/JoshMJohns [link] [comments]
View originalFirst thing you see when Googling "OpenAI Codex app" is a fake malware website
submitted by /u/vashchylau [link] [comments]
View original95% of the agents posted here would be dead within 24 hours of real production traffic and it's not the model's fault
I've spent 18 months building agent infrastructure and watched a lot of impressive demos. Here's the uncomfortable pattern: the demo works beautifully, the founder posts it, everyone claps and then it touches real users and quietly dies. Not because GPT-5 / Claude / whatever isn't smart enough. The model is almost never the problem anymore. It dies for three boring reasons nobody wants to talk about because they're not sexy: 1. AMNESIA. Your agent forgets everything the moment the process restarts. Crash, redeploy, pod cycle gone. So everyone hacks together a pickle file or a Postgres table, and it works until they have more than one agent and the memory needs to be shared. Then it's a mess. 2. SUICIDE BY LOOP. An agent has no idea it's in a loop. It will call the same tool with the same args 400 times and cheerfully burn $200 of tokens overnight, because it has no metacognition. It literally cannot detect its own failure. The defense has to live OUTSIDE the agent and almost nobody builds that. 3. NO BLACK BOX. The agent does something weird in front of a customer. They ask "why did it do that?" and you stare at logs that show inputs and outputs but no chain of reasoning. You have no answer. Trust evaporates. The whole industry is obsessed with the brain (the model and ignoring the nervous) system (memory, the immune system (loop detection), and the flight recorder (audit).) The unsexy truth: the next wave of agent winners won't have better prompts. They'll have better infrastructure. The model is commoditising. The reliability layer is where the actual moat is. I got annoyed enough about this that I built the layer myself persistent memory, automatic loop detection, and a tamper-evident audit trail, framework-agnostic (LangChain/CrewAI/AutoGen/OpenAI/MCP. It's at) octopodas.com if you want to tear it apart genuinely want feedback from people who've shipped agents and hit this wall. But honestly even if you never touch my thing: stop optimising the prompt and start thinking about what happens when your agent restarts, loops, or gets asked "why." submitted by /u/DetectiveMindless652 [link] [comments]
View originalI run 30+ Claude, Codex, and Antigravity sessions in parallel. Here's the v4 of the tool I built to keep them straight.
Why I built it in the first place. I've found myself running many agent sessions in parallel, just because I couldn’t stand waiting for each turn, and always had ideas/features for more things to build meanwhile. I started from multiple terminals, but I quickly lost track of conversations, lost time because sessions were blocked on me, and overall had a big headache at the end of each day 😂 [and fewer hours of sleep, still working on this one :) ]. So I built a local dashboard for myself, then for some friends, and it grew into CCC (Command Center for Claude). v4 shipped a few days ago. Another big bonus is that you see from day 1 all sessions that you have ever run on your machine. All the IDEs (Codex included) tend to only show sessions started by them. Key features in v4: Antigravity support alongside Claude and Codex. Including the app-only sessions other tools can't drive. CCC bridges the local language-server cascade RPC inside the Antigravity window, so a session you started by clicking around in the app shows up in the same inbox as your terminal-spawned ones. GitHub integration - worktrees, click-to-fix issues, commit-and-close: Worktrees support: every session can run in its own worktree so parallel agents don't step on each other GitHub issues in your CCC inbox; spawn an agent to fix one with a click Commit with a comment that closes the issue, all from the conversation Activity indicator right from the conversation list: You can see at a glance what each agent is doing right now, without opening the terminal. Multi-session group chat. This is a super fun and useful feature which became my go-to behavior when I want to vet a decision (coding, strategy, life choices :) ). Also useful when you have sessions that worked on the same thing in different periods of time, and you want to bring them up-to-speed: Put them in a group chat and they’ll start filling each other in. You (@human) can guide them, help them make decisions etc. Sessions can also ask/chat with other sessions 1:1. Spawn a new "Agent" from an existing session - simply say "spawn a new /ccc-orchestration session about " to offline work into another session. Formatting for easy reading and writing: Two conversation panes side-by-side (drag a conversation into the drop target on the right) Pop-out windows (drag a conversation into its own native window) MD files render inline (no more cat README.md walls of text) Tables, code blocks, and rich formatting render properly in the conversation pane Read-aloud TTS with word-by-word highlighting, great for skimming long agent outputs in the background Per-session background colors so you can tell sessions apart at a glance File cabinet on the right rail surfaces files each session touched Smart session naming, "Open in terminal / Claude Desktop" Sibling-worktree detection, Conversation row pinning. More in the repo changelog. Open source, MIT, vanilla JS + Python stdlib, no cloud, no account, no telemetry by default. Simply runs on localhost:8090. Install (macOS) - Three options: brew tap amirfish1/ccc brew install ccc (or curl -fsSL https://raw.githubusercontent.com/amirfish1/claude-command-center/main/scripts/install.sh | CCC_FROM=reddit bash if you don't have Homebrew) the signed .dmg if you'd rather not touch a terminal (Native Mac app). Drag the app to Applications, double-click. You know the drill. Happy to answer setup questions in the thread or in DM! The Antigravity bridge is the piece I most want real-user feedback on before the Show HN on Thursday. submitted by /u/Mediocre-Thing7641 [link] [comments]
View originalChrome extension built with Claude in one session. It tracks how much energy and water AI queries use
I was curious how much electricity and water my AI queries actually consume, so I asked Claude to help me build a Chrome extension to track it. What started as "can you make a content script that detects when I send a query" turned into an entire multi-session build that shipped to the Chrome Web Store. The whole thing was built collaboratively in Claude: architecture, detection logic, energy calculations, popup UI, dark mode, i18n (8 languages), the App Store assets, even the promo screenshot. Claude wrote the code, I tested and gave feedback, we iterated. The extension estimates GPU compute, water (datacenter cooling + power generation), and CO₂ per query, then shows equivalents like phone charges and glasses of water. Everything runs locally with no accounts, no data sent anywhere. And… the extension tracked its own energy cost while helping build itself. Peak meta. Free, open to feedback: https://chromewebstore.google.com/detail/footprint-ai/pdfdnbhdpklnpicmmnbjgcgffekgdebe Also have Firefox and Safari versions available.
View originalAI coding agents are creating a secret leakage crisis and nobody's talking about it seriously yet
This isn't a doomer post. It's a pattern I've been watching closely and people does as well and I think it's worth an honest discussion. The old model of secret leakage was human error. Developer moves fast, forgets to add .gitignore, commits a .env file, moves on. Happens, but it's recoverable, it's traceable, and most teams with basic hygiene catch it. The new model is different. AI coding agents Cursor, Copilot, Devin, Claude in agentic mode, pick your flavor write, commit, and push code at a speed no human review process was designed to handle. They don't have security intuition. They have pattern completion. And the patterns they've learned from are full of examples where credentials live in config files, environment strings get hardcoded "temporarily," and API keys appear inline because that's what the training data showed works. Here's what's actually changing: **Volume.** A developer using an agent ships 3 to 5x more code per day than without one. That's 3 to 5x more surface area for mistakes per developer per day. **Review gaps.** Nobody carefully reviews AI generated code the way they review handwritten code. The psychological contract is different "the AI wrote it" creates a diffusion of responsibility that security doesn't survive. **Commit frequency.** Agents that push directly (and more teams are allowing this) bypass the natural pause where a human might notice something before it hits the remote. **Context blindness.** An agent given a task like "integrate Stripe payments" will do exactly that including pulling in the live key from wherever it can find it, because that's what completes the task. I've been building a tool that scans for exactly this class of problem and the number of exposed credentials I'm seeing in repos created in the last 6 - 12 months versus repos from 3+ years ago is not subtle. The slope is steep. The solutions people reach for pre commit hooks, secret scanning in CI were designed for human paced development. They're not keeping up. Curious if others are seeing the same patterns. What's your team doing about this, if anything? *(For context: I built* [*SecOpsium*](https://secopsium.com)*, a security validation platform that catches this class of exposure CLI is open source at* [*github.com/secopsium/secopsium-cli*](https://github.com/secopsium/secopsium-cli) *if you want to look under the hood. Not the point of this post but figured I should be transparent.)*
View originalYes, BuildShip offers a free tier. Pricing found: $0 /month, $0 /year, $19 /month, $225 /year, $29 /mo
Key features include: Describe Your Idea and Watch AI Build it, Tweak and Test Your Flow Logic Visually, Deploy Your Way, Host or Self-Host, Full code access, Secure Auth Keyless Prototyping, Self-host under your infrastructure, Version Control with GitHub, Logs, Monitor Status, Alerts and more.
BuildShip is commonly used for: Automating HR onboarding processes, Streamlining finance report generation, Creating automated marketing campaigns, Managing customer support ticketing systems, Building data dashboards for real-time analytics, Integrating with CRM systems for lead management.
BuildShip integrates with: Zapier, Slack, Google Sheets, Salesforce, Mailchimp, Trello, Jira, AWS, Microsoft Teams, Stripe.
Based on user reviews and social mentions, the most common pain points are: API bill, API costs, cost tracking, cost per token.
WORLD's FIRST HATATHON
Jul 23, 2025
Based on 159 social mentions analyzed, 9% of sentiment is positive, 89% neutral, and 1% negative.
