Your coding agent says "it's fixed." But is it, really? When AI agents fail, the problem usually isn't the model — it's the harness. In EP2 of #AgenticTalks#, Qoder reveals the 5-point fix. Aug 13, live on Alibaba Cloud. Register → #AlibabaCloud# #AICoding# #Qoder#

The future of coding is AGENTIC. Thanks to all who joined our Qoder webinar! See how Qoder, our end-to-end AI coding agent, transforms dev workflows & boosts productivity. Try Qoder on us to streamline your dev lifecycle! 🔗 #Qoder# #AICoding# #AlibabaCloudPH#

🤖 AI writes code faster, but are we producing vulnerabilities faster? With AI generating over 40% of new code, traditional post-scanning just can't keep up. That’s why we built Qoder Security—shifting security left directly into the coding session. The Impact: • +60% vulnerability detection rate • -80% false-positive alerts • Hours to fix, not weeks From real-time regex interception to cross-file deep review, we’re embedding security natively into your workflow. 🚀Security starts with the first line of code. Try it out in Qoder Desktop & CLI today! Qoder Desktop (IDE) guide:  Qoder CLI guide:  #Qoder# #AICoding# #QoderSecurity# #CyberSecurity# #AgenticCoding#

# Cursor Features and Practical Usage 🧩 Tired of re-explaining the same multi-step workflow to your agent every single time? Cursor Skills let you package repeatable workflows so the agent just knows how to do them. 🏷️ Title: Reusable Workflows (SKILL.md) 🔗 URL: 📘 Overview Skills are portable, version-controlled packages that teach agents how to perform domain-specific tasks. They bundle scripts, templates, and reference material that the agent runs through its available tools. They are the evolution of Rules: agents can apply them automatically based on context, or you can invoke them explicitly as slash commands. ⚙️ How It Works Each skill is centered on a `SKILL.md` file whose leading YAML frontmatter defines its behavior. ・`name`: a lowercase identifier that must match the parent folder name (required) ・`description`: what the skill is for and when it applies; the agent reads this to decide whether to use it (required) ・`paths`: glob patterns that scope the skill to matching files (optional) ・`disable-model-invocation`: set to `true` to make it slash-only, included only when you type `/skill-name` (optional) Discovery is hierarchical: project skills live in `.cursor/skills/` (or `.agents/skills/`), and global ones in `~/.cursor/skills/`. The root is walked recursively, so nested subdirectories are found too. A skill folder can also ship `scripts/` (executable code), `references/` (docs loaded on demand), and `assets/` (templates or images). 🛠️ Practical Usage For example, in `.cursor/skills/api-endpoint/SKILL.md` set the frontmatter `name` to `api-endpoint`, a `description` of the workflow, and `paths` scoped to `src/api/**/*.ts`, then write the steps in the body (register the route, validate input with a zod schema, always add tests). If you do not want automatic invocation, add `disable-model-invocation: true` and call it explicitly by typing `/api-endpoint` in Agent chat. 💡 Use Cases In a monorepo, placing a `.cursor/skills/` folder inside each app automatically scopes those skills to files in that directory, so you can skip `paths` entirely. Share release procedures, migration scripts, or code-review checklists so the whole team works the same way. You can convert existing assets with the built-in `/migrate-to-skills` (Cursor 2.4): "Apply Intelligently" rules (`alwaysApply: false`) become skills, and slash commands become skills with `disable-model-invocation: true`. ⚠️ Caveats A skill's identity comes from the folder containing `SKILL.md`, not any parent category. The old `globs` field is deprecated; use `paths` now. `/migrate-to-skills` does not migrate `alwaysApply: true` rules or user-level rules, so those need manual handling. #Cursor# #AICoding#

# Antigravity Features and Practical Usage 🚀 "Think before doing" with Plan, "just do it" with Fast. Match caution and speed to the nature of each task. 📌 Title and Feature URL Title: Plan / Fast モード URL: 📝 Overview Antigravity has two execution modes. Plan mode generates a detailed plan (a Plan Artifact, the Implementation Plan) before acting and implements only after approval. Fast mode skips the planning phase, interpreting your request and executing immediately. The basic split is Plan for complex tasks, Fast for minor fixes. 🔧 How It Works The two modes behave as follows: - Plan mode: it analyzes scope and examines files, then generates an Implementation Plan covering the goal, technology choices, steps, file modifications, and testing approach. It implements after approval and records changes in a Walkthrough. - Interactive approval: highlight a section of the plan and comment; the agent incorporates it before implementation begins. - Fast mode: on a "just do it" philosophy, it executes immediately without plans or waiting for approval, then reports results. - Switching modes: use the interface controls or the keyboard shortcut (Cmd/Ctrl + .); the current mode shows in the input box. 🛠 Practical Usage - For complex refactors or production-affecting changes, approve the plan in Plan mode before starting. - For minor work like typo fixes, variable renames, or boilerplate, execute immediately in Fast mode. - Combine them: do foundational work in Plan, then switch to Fast for refinements. - Mode switches apply only to new requests; ongoing tasks continue in their original mode. 🎯 Use Cases - Approve a large refactor as a plan Artifact in Plan mode, then implement safely. - Knock out adding a button or fixing a typo instantly in Fast mode. - Confirm the approach in Plan for unfamiliar tech, then move to Fast once patterns are set. - Keep oversight via Plan for changes to production or critical systems. ⚠️ Caveats - Fast has no planning phase to resolve ambiguity, so be specific and keep to a single task rather than bundling many. - Fast trades thoroughness for speed and is not suited to critical changes. - Mode switches are not retroactive; switching takes effect from the next request. #Antigravity# #AIcoding#

# Cursor Features and Practical Usage 📏 "Always validate APIs with zod" — you shouldn't have to retype that policy every prompt. Cursor's Rules give the AI persistent memory. 🏷️ Title: Persistent Instructions (Project/Team/AGENTS.md) 🔗 URL: 📘 Overview Rules provide persistent, system-level instructions to Cursor's Agent. Since LLMs don't retain memory between completions, Rules supply reusable context at the prompt level. They bundle prompts, scripts, and guidance so workflows stay reusable across a team. ⚙️ How It Works Cursor supports several kinds of Rules. ・Project Rules: `.mdc` files under `.cursor/rules`, version-controlled and scoped to your codebase. ・User Rules: global preferences defined in Cursor Settings, applied across all projects. ・Team Rules: org-wide rules managed from the dashboard (Team/Enterprise plans). ・AGENTS.md: a plain-markdown alternative in the project root or subdirectories, no frontmatter needed and nestable (more specific instructions take precedence over parent ones). Project Rule frontmatter controls behavior: `alwaysApply: true` applies to every chat, `globs` with `alwaysApply: false` auto-attaches when matching files are in context, `description` only lets the Agent apply it when relevant, and with none of these it applies only via `@`-mention. 🛠️ Practical Usage ・Scope rules to file types with globs, e.g. `src/**/*.tsx`, comma-separating multiple patterns. This auto-applies "always validate APIs with zod" whenever matching files are edited. ・Generate rules in chat with `/create-rule`, or via `Cursor Settings > Rules, Commands` → "+ Add Rule." ・Example `.mdc`: a frontmatter with `globs: src/api/**/*.ts` and `alwaysApply: false`, followed by rules like "validate every incoming payload with a zod schema" and "on failure, return 400 with a unified error shape." ・For teams, remember the application order Team Rules → Project Rules → User Rules, and enforce company-wide conventions via Team Rules. 💡 Use Cases Turn repeated policies — "validate APIs with zod," "use Conventional Commits," "structured JSON logging" — into Rules, auto-applied only to matching files via globs. Check them into Git so the whole team benefits from the same conventions. ⚠️ Caveats Keep each rule under 500 lines and split larger ones. Avoid copying style guides (use linters), and prefer referencing files over duplicating content. Target frequent patterns, not rare edge cases. Note that User Rules apply only to Agent (Chat) — not to Inline Edit, other AI features, or Cursor Tab. #Cursor# #AICoding#

# Cursor Features and Practical Usage ✏️ No chat needed — select code and say "make this async" right where it lives. Cursor's Inline Edit is the shortest path to local fixes. 🏷️ Title: Cmd/Ctrl+K 🔗 URL: 📘 Overview Inline Edit lets you make focused code changes directly in the editor without opening the chat. Select code and press `Cmd+K` (Mac) / `Ctrl+K` (Windows/Linux), and an instruction box opens in place. It's built for fast, pinpoint edits. ⚙️ How It Works ・Give a natural-language instruction on the selected code and press `Return` to apply, e.g. "Convert this to an async function." ・It supports iterative refinement — request follow-up edits and they apply sequentially. ・Question mode: press `Opt+Return` (Mac) / `Alt+Return` (Windows/Linux) to ask about the selected code instead of editing it. If you want to implement a suggestion, type "do it" and confirm. ・Escalate to Agent: for larger, multi-file changes, select code and press `Cmd+L` (Mac) / `Ctrl+L` (Windows/Linux) to open Agent with your selection as context. 🛠️ Practical Usage ・Select a function and `Cmd+K` → "make it async," "add type annotations," "turn this branch into an early return" — cycle through local rewrites fast. ・When behavior is unclear, switch to question mode with `Opt+Return`, ask "what does this regex match?", then type "do it" to move to a fix if needed. ・When a fix grows complex and spreads across files, hand the selection to Agent with `Cmd+L` and escalate to autonomous implementation. 💡 Use Cases Quickly fix a single function flagged in review with `Cmd+K`, verify shaky logic in question mode before changing it, and promote to Agent once impact widens — a staged workflow that fits the size of the change. ⚠️ Caveats Inline Edit is meant for local, focused edits. Rather than forcing broad changes through `Cmd+K`, hand off to Agent with `Cmd+L` early for safer, more reliable results. Also note that User Rules generally apply only to Agent (Chat), not to Inline Edit. #Cursor# #AICoding#

# Antigravity Features and Practical Usage 🚀 Check an agent's work through visible deliverables, not piles of logs. Artifacts solve the trust problem in a verifiable form. 📌 Title and Feature URL Title: Artifacts URL: 📝 Overview Artifacts are tangible deliverables that agents generate. They surface as visible outputs—task lists, implementation plans, screenshots, browser recordings, and code diffs—so you can verify the agent's logic at a glance. Instead of reading endless logs, you review the deliverable itself. 🔧 How It Works Artifacts come in several kinds with built-in feedback: - Implementation Plan: a rich markdown file covering goal, technology choices, steps, file modifications, and testing approach. - Task List: a structured breakdown of the work. - Walkthrough: a summary of completed changes and next steps. - Code diffs, screenshots, browser recordings, architecture diagrams, and images. - Commenting: like a Google Doc, you select a section and comment, and the agent incorporates your input without stopping execution. 🛠 Practical Usage - Highlight a section of a plan Artifact and leave an inline comment to steer the approach. - Use screenshots and browser recordings to confirm UI and behavior with evidence. - Review the scope of changes via code diffs before incorporating them. - Control Artifact review policies in project settings. 🎯 Use Cases - Review the plan Artifact before implementation, fine-tune via comments, then approve. - Keep a browser recording of an E2E test as proof of behavior. - Review multi-file change diffs in one place to reduce review load. - Use the Walkthrough afterward to grasp what changed and what is next. ⚠️ Caveats - Comments are applied without halting work, so give feedback early on critical changes. - Skipping artifact review turns autonomy into a lack of oversight; make review policies explicit in settings. - Browser recordings assume Chrome integration. #Antigravity# #AIcoding#

# OpenCode Features and Practical Usage ✨ "Plan first, then build." Learn OpenCode's core operations and you get a development rhythm where you can confidently delegate to the AI while always being able to roll changes back. 🏷️ Title: @/search / Plan & Build / images / /undo / /redo 🔗 URL: 📘 Overview Day-to-day work in OpenCode boils down to a handful of basics: referencing files, switching agent modes, sending images, and undo/redo. Master these and interactive coding becomes smooth. ⚙️ How It Works ・File references with `@`: Typing `@` in a message triggers a fuzzy search over filenames so you can pull a file into context and ask about it. Prefixing a message with `!` runs a shell command and feeds its output back as a tool result. ・Plan vs Build: Plan is a planning mode where edits and bash are restricted to "ask" (confirm first); Build is the implementation mode with access to all tools. Press `Tab` to switch between them. ・Image input: Drag and drop an image into the terminal to pass visual context such as an error screen or a design mockup. ・`/undo` and `/redo`: `/undo` reverts the last message and the file changes it made, and can be run multiple times to go further back. `/redo` reapplies an undone change. 🛠️ Practical Usage 1. Start in Plan mode with something like "draft a refactor plan for `@/src/api/handler.ts`" and review the plan. 2. Once satisfied, press `Tab` to switch to Build and let it implement. 3. If the result is off, `/undo` to roll back, and `/redo` if you change your mind. In short: `@/src/payment.ts` for a file reference, `Tab` to go Plan to Build, `/undo` to revert (repeatable), and `/redo` to reapply. 💡 Use Cases For high-impact refactors, lock down the approach in Plan first, then implement in Build — it is far safer. Because any unexpected AI change can be rolled back instantly with `/undo`, the cost of experimentation drops sharply. ⚠️ Caveats ・`/undo` rolls back per message; be careful when mixing it with manual edits. ・Even in Plan mode, if edits are configured as "ask," approving the prompt will still apply changes — review carefully before confirming. ・Image drag-and-drop support depends on your terminal emulator. #OpenCode# #AICoding#

# Cursor Features and Practical Usage 🧭 Code now, plan first, or just investigate? Cursor lets you switch the agent's behavior with a single `Shift+Tab`. 🏷️ Title: Agent Modes (Shift+Tab) 🔗 URL: 📘 Overview Cursor's agent has multiple modes so its behavior matches the task at hand. Press `Shift+Tab` in the chat input to rotate through modes, or pick one from the mode dropdown. When Cursor detects complex work, it may automatically suggest Plan mode. ⚙️ How It Works ・Agent mode: the standard implementation mode. Best for quick changes or familiar tasks where jumping straight into code makes sense. ・Plan mode: builds a plan before coding. The agent asks clarifying questions to understand requirements, researches your codebase to gather context, and generates a comprehensive plan for review. The workflow is: Questions → Research → Plan Creation → Review/Edit → Build. ・The plan is editable in chat or as markdown, and you proceed to build only once satisfied. Plans save to your home directory by default, but you can move them into the workspace for team collaboration. 🛠️ Practical Usage ・For features that ripple across many files, generate a plan in Plan mode, approve it, then implement — this minimizes rework. ・When you only want to investigate, use a read-only style mode and ask "How does this auth flow work?" to understand the current state without edits. ・For debugging, paste the stack trace or logs and walk through hypothesis generation → root-cause identification → fix strategy for steadier diagnosis. ・Use `Shift+Tab` to cycle modes quickly and match the granularity of the task. 💡 Use Cases Approve the big picture in Plan mode before a large refactor, investigate safely in a read-only mode, and pin down bugs from a pasted error to root cause. Splitting work into phases keeps the scope you delegate to the AI under control. ⚠️ Caveats The docs stress a key principle: when the agent's output misses the mark, reverting the changes and refining the plan is often faster than chasing fixes with follow-up prompts. Figuring out what to change is the hard part — delegate the implementation to Agent. #Cursor# #AICoding#