[Antigravity] Improve UX for deployments and CM

What They Accelerate, What They Break, and How I’d Run Them in Your Org Over the past 14 months, I’ve been counseling startups to quickly incubate new ideas and helping Fortune 500 transform their existing AI pipelines. Along the way I’ve learned a lot of new concepts, tactics and software available to do things not possible only a few years ago. One of the more transformative trends I’ve seen recently is in the AI IDE space. AI now writes a meaningful percentage of first-draft code for serious engineering teams. The novelty phase is definitely over. The real question in 2026 is not “Can AI code?” It’s: How does AI change your operating model, ie: velocity, governance, architecture, and risk? I’ve been using the several services for a few months now, but mainly focusing on four: Replit — outcome-first (prompt → running app + deploy) Cursor — developer-first (repo-aware acceleration inside VS Code) Claude Code — delegation-first (multi-attempt orchestration) Google Antigravity — infrastructure-aware, multi-agent experimentation at IDE scale I don’t consider these services direct competitors, but they do optimize for different leverage points. The Shift: Coding Is Easier. Infrastructure Is Still Hard. Across all four tools, one pattern holds: First drafts are crazy fast. Iteration loops shrink. Templates vanishes. Architectural mistakes compound faster if you lack discipline or first develop a plan. Bottom line: AI lowers the cost of generating software. It raises the cost of weak engineering governance. That’s the context for my analysis. Let’s jump into it. 1. Replit — Outcome First (Prompt → Running App) When I used Replit for the AdeptEPR AI prototype, it wasn’t for a cute demo dashboard. I used it for the hard part: standing up an end-to-end prototype that combined document-grounded compliance AI Q&A with a real ingestion → cleansing → reporting workflow. My goal was simple enough: build something credible enough to show a buyer like a compliance lead or packaging team and say, “This is exactly how the platform behaves, it’s not a black box.” The Replit prompt pattern that worked for me I stopped writing “make me an app” prompts and started writing non-negotiable product requirements inside the prompt before hitting “compile” the same way I would write acceptance criteria. I literally led with constraints like: The AI must answer ONLY from EPR documents uploaded into the tenant library If it can’t find support, it must say so and request the missing section Every factual claim must include a citation: document + page + heading No web browsing. No external knowledge. No best guesses That one constraint block did more for prototype credibility than any UI polish ever could. What I built in Replit for AdeptEPR AI 1) EPR Expert (RAG chat that can’t hallucinate): This was the centerpiece. The chat experience wasn’t “ask anything.” It was compliance-grade behavior: Upload PDFs/DOCs into a tenant library Parse + chunk + embed (tenant-scoped) Ask questions and get answers with inline citations A “View Sources” panel showing the excerpt used A coverage indicator: which docs were searched If the answer wasn’t in the uploaded docs, the assistant politely refused and asked for the missing doc/section. 2) Packaging ingestion + cleaning that isn’t a black box: I added an ingest wizard that followed the real world: Upload raw CSV/XLSX packaging data Map columns to a canonical schema (SKU, component, material, weight, units, recycled content %, state, year…) Validate types and required fields Detect common issues (bad state codes, inconsistent units, missing weights, duplicates) Propose fixes as suggestions Human-in-the-loop accept/reject Publish a canonical dataset into a filterable table That “accept/reject” moment matters. It signals control and auditability. 3) State + year CAA reports generated from canonical data. The reporting flow was: Select state + reporting year Generate a report table from canonical data Rollups by material category Warnings when data gaps block compliance-grade output Export to CSV/XLSX Generate a “report package” (report + data quality summary + citations index) Most importantly, the report logic was designed to reference rule sources from the uploaded documents so the whole thing could remain explainable. 4) RBAC that matches how customers operate, I added a clean, two-role model: ADMIN: create users, upload raw data, run cleansing pipeline, generate/publish reports USER: use chat, review data read-only, view/download reports I enforced it server-side, not by hiding buttons. 5) A dashboard that makes it feel like a real product: After login, the landing page had four cards that mapped to the business flow: EPR Expert → opens the doc-only chat Action Items → assigns follow-ups from Admin to Users (deadlines, tasks) EPR Project Status → per-state status table (Register / Reports / Payment) with Red/Yellow/Green + dates CAA Reports → recent published reports + downloads That dashboard is what turns “prototype” into “platform narrative.” My favorite Replit features for this kind of work 1) It creates the whole stack in one place: I could prototype “product reality” — auth, storage, workflows, exports — without spending a week on scaffolding. 2) It’s naturally demoable: One link. Stakeholders can click the actual experience. That’s a huge edge in product conversations. 3) It tolerates heavy prompting: Replit is one of the few environments where I can drop in a long, PM requirements-heavy build prompt, including guardrails, and the tool responds by scaffolding a coherent codebase. (rounds of 40 minutes) A few example prompts that are “PM-friendly” Even if you’re not technical, prompts like these will get you somewhere meaningful: “Build a docs-only compliance chat that refuses to answer without citations.” “Create an upload → mapping → validation → suggested fixes workflow for a CSV.” “Show a filterable canonical table with issue tags and export.” “Generate a state/year report with rollups and warnings if data is incomplete.” “Enforce Admin vs User permissions server-side.” That’s the point: product managers can describe the workflow in English and get a clickable artifact. Where Replit stopped being my tool of choice Once the prototype moved from “credible demo” to “long-term codebase,” the challenges started to show: It’s easy for agent-driven development to drift architectures Cost/usage can get unpredictable when iterating heavily You need strong discipline to prevent duplicated patterns At that point, I transitioned the ongoing repo work into Cursor and used Claude for orchestration. My bottom line on Replit (based on AdeptEPR AI): Replit is the best tool I’ve used for turning a product spec into a working, shareable prototype, especially when the prototype has real workflow depth: RBAC, ingestion, data quality, and reporting. It’s not just “vibe coding.” If you write the prompt like a product leader — with guardrails and acceptance criteria — it can produce something that behaves like a real system. 2. Cursor — Developer First (Repo-Aware Acceleration) Cursor is a VS Code–based IDE rebuilt around AI. Its differentiator is codebase indexing. It understands your repo. Where Cursor excels Multi-file refactors Type-aware edits Pattern consistency High-quality multi-line autocomplete Tight chat-to-diff workflow Cursor doesn’t try to eliminate engineering. It amplifies engineers. What it doesn’t change You still own architecture You still provision infra You still run CI/CD You still debug production issues It accelerates mechanical work. It does not remove lifecycle complexity. Cursor is strongest where code is the product. 3. Claude Code — Delegation First (Multi-Attempt Orchestration) This is where I spent most of the 2H of 2025. Claude Code is the preferred tool within my circles at MIT. It changes the mental model entirely. The right way to use it is not “generate perfect code” it’s “delegate threads of work.” My path to success: Attempt 1: Mostly wrong, but clarifies constraints Attempt 2: Directionally useful Attempt 3: Usable foundation Claude behaves like a capable junior engineer who forgets context between sessions unless you provide it explicitly. Paint the room white, if you remember that joke. Where Claude excels Architectural reasoning Running multiple development threads in parallel Pre-reviewing code Generating structured plans Where it breaks No persistent memory unless you manage context files Confidently incorrect outputs Context window constraints on large systems Non-trivial usage cost Claude increases leverage, but only for disciplined teams. It rewards documentation, decomposition, and review rigor. 4. Google Antigravity — Experimental Multi-Agent System Builder What took this article so long to publish was the recommendation by one of my engineering teams to review Antigravity. I didn’t want to use it for a simple CRUD app. I used it to prototype a new Supply Chain Risk Project, a startup concept focused on multi-tier supplier visibility, risk scoring, and event monitoring. A product I know well from my time with Interos. The goal wasn’t just to generate UI. It was to see whether a multi-agent, plan-before-code IDE could meaningfully accelerate: Entity graph modeling Risk scoring logic Event ingestion flows Dashboard visualization Alert workflows Antigravity is the most ambitious of the tools I tested. I’m actually kindof in love with it. It acts less like autocomplete and more like a coordinated AI engineering team. What I Asked Antigravity to Build Instead of prompting for a single component, I gave it a system-level instruction: “Design a supply chain risk platform with multi-tier supplier mapping, event ingestion, risk scoring, and a dashboard that surfaces country, financial, and geopolitical exposure.” Because Antigravity uses Gemini with a massive 2M-token context window, I could include: Data model assumptions Risk category definitions (financial, ESG, geopolitical, natural hazard) Mock supplier relationships Sample event feed structures Dashboard requirements Alerting conditions The size of the context window changed the feel of the session. I didn’t have to constantly re-explain constraints. Where Antigravity Felt Powerful 1. Plan-before-code actually matters Antigravity’s multi-agent workflow starts with planning. It didn’t just jump into writing files. It: Proposed a system architecture Suggested entity relationships (Supplier, Facility, Product, RiskEvent) Outlined risk scoring aggregation logic Identified where test coverage should sit That felt different from “write me a component.” It felt like assigning the below to the same person: A planner An implementer A tester A debugger For a supply chain risk system, which is inherently cross-domain, this planning step was valuable. 2. Handling graph-style relationships The Supply Chain Risk Project requires modeling: Tier 1 → Tier 2 → Tier 3 relationships Shared facilities Country-level exposure rollups Because Antigravity could reason over a large shared context, it handled entity relationships better than smaller-context tools in a single pass. For example, I asked: “Aggregate country-level exposure risk weighted by supplier revenue concentration and event severity.” It scaffolded: A revenue-weighted scoring function Event severity multipliers Country rollups A dashboard widget displaying composite exposure That kind of cross-file, cross-domain logic is where the larger context window helped. 3. Browser-aware debugging When I built the risk dashboard (country heatmap + risk category table), Antigravity could: See UI errors React to rendering issues Adjust component logic That closed the loop between frontend and backend faster than traditional IDE flows. Where It Broke (and It Did) Antigravity is still experimental. During the Supply Chain Risk prototype, I experienced: Frozen UI panels Buttons not responding Broken state after larger multi-file edits Limitations due to extension support gaps For example, I attempted to layer in more advanced data tooling, and the ecosystem constraints became visible quickly. There were also usage ceilings that appeared after heavy multi-agent sessions — something that matters if you’re prototyping aggressively. What It’s Really Good For (Right Now) Based on using it in the Supply Chain Risk Project, Antigravity is strongest at: System-level planning Multi-file scaffolding Connecting architecture + tests + UI Large-context reasoning Early-stage product modeling It is not yet a stable, production-ready daily driver like Cursor. Right now, it feels like: An R&D lab for AI-coordinated software assembly. Prompts That Worked Well for the Supply Chain Risk Project For product leaders or PMs who want to experiment, try structured prompts like: “Design a multi-tier supplier graph with revenue-weighted risk scoring.” “Create a risk engine that combines financial health, geopolitical exposure, and event severity.” “Build a dashboard that shows country-level exposure with Red/Yellow/Green status.” “Plan the system architecture before generating code.” Antigravity responds best when you ask it to think in systems, not feature widgets. Where I Would Use It in a Real Startup For the Supply Chain Risk Project, I’d use Antigravity for: Early architecture exploration Generating alternative scoring models Stress-testing system design assumptions Rapidly iterating on dashboard concepts I would not yet rely on it as the core production IDE. My Bottom Line on Antigravity If Antigravity stabilizes and expands its ecosystem, it could become the most strategically important of the four — because planning, orchestration, and infra-level reasoning are more valuable long-term than autocomplete speed. But today? It’s powerful, experimental, and best used intentionally — especially when modeling something as structurally complex as a supply chain risk platform. And that distinction matters if you’re leading technology in 2026. Antigravity is the most ambitious. Cursor is the most production-reliable. Claude is the most orchestration-flexible. Replit is the fastest to artifact (not production) What This Means for a Real Organization If I were running product and technology in 2026, I would not “pick one,” I would create lanes: Prototype Lane Replit and Antigravity allowed. Speed > purity. Output disposable. Product Lane Cursor primary. Strict review standards. AI acceleration, not architectural improvisation. Delegation Lane Claude for refactors, migration scripts, doc generation, parallel tasks. Explicit context artifacts required. The Leadership Reality All four tools make it easier to generate code. None eliminate: Architectural responsibility Security accountability Deployment complexity Incident ownership The risk profile has shifted. The new danger is not slow engineering. It’s fast, confident, poorly-governed engineering. TL;DR Replit proves ideas quickly. Cursor makes engineers faster. Claude multiplies delegation. Antigravity experiments with AI-coordinated system assembly. If Antigravity matures, I feel it will become the most structurally important, because planning, testing, and infra composition are higher-order concerns than autocomplete. But today? Cursor is the safest daily driver. Claude is the most strategically flexible. Replit is the fastest to show. Antigravity is the most exciting — and the least stable. In every case, the differentiator isn’t the tool. It’s the operating model around it. And that is still very much a human responsibility.