DEV Community: Evan-dong

Stop Asking Claude Code for Markdown Specs. Ask for HTML Artifacts.

Evan-dong — Sat, 09 May 2026 09:30:17 +0000

Claude Code is very good at writing Markdown. That does not mean Markdown should be the default output for every task.

Thariq from the Claude Code team recently described a workflow where he increasingly asks Claude Code for HTML instead of Markdown. The reason is practical: long Markdown specs are easy to generate but hard to read. HTML can turn the same information into a navigable, visual, and sometimes interactive artifact.

When HTML Beats Markdown

Use HTML when the output is meant to be consumed by people, not maintained line by line in Git.

Good fits:

PR walkthroughs
design option comparisons
architecture explainers
onboarding docs
debugging reports
one-off planning tools
draggable prioritization boards

Keep Markdown for:

READMEs
changelogs
durable docs
API references
anything that needs clean Git diff review

Example: PR Review Artifact

Instead of:

Summarize this PR in Markdown.

Try:

Create a single self-contained HTML PR walkthrough.
Render the important diff areas with inline annotations.
Color-code findings by severity.
Add a manual verification checklist at the bottom.

That gives reviewers something closer to a focused review interface than a wall of bullets.

Example: Implementation Options

Generate 5 implementation approaches as one HTML file.
Use a comparison grid.
For each approach show:
- complexity
- migration risk
- test impact
- recommended use case

This is much easier to scan than five Markdown sections stacked vertically.

Trade-Offs

HTML is not always better.

Concern	Markdown	HTML
Git diffs	Great	Noisy
Long-term docs	Great	Mixed
Visual hierarchy	Limited	Strong
Interactivity	None	Possible
Sharing in browser	Requires renderer	Native

The rule I use: Markdown is the source. HTML is the reading surface.

Practical Prompt

Create a self-contained HTML explainer for this feature.
Audience: an engineer who has not seen this code before.
Include a visual summary, annotated code snippets, risks, and a next-step checklist.
Do not add external dependencies.

The real insight is not "HTML is better than Markdown." It is that AI-generated output does not have to be plain text. If the model can generate a useful interface, ask for the interface.

For teams building Claude Code workflows across multiple models, EvoLink provides unified API access to Claude and other frontier models.

OpenAI's New Realtime Voice Models Can Think, Translate, and Transcribe — Here's What Developers Need to Know

Evan-dong — Fri, 08 May 2026 13:36:06 +0000

OpenAI just shipped three realtime voice models through their API. One reasons at GPT-5 level during live calls. One translates 70+ languages in real time. One does streaming transcription. All available today.

Let me break down what matters for developers.

The Three Models

GPT-Realtime-2 handles voice conversations with GPT-5-level reasoning. The key difference from previous voice models: it can call tools mid-conversation without going silent. It narrates what it's doing while executing — OpenAI calls this "preamble."

GPT-Realtime-Translate does real-time voice translation. 70+ input languages, 13 output languages. End-to-end audio processing (no intermediate text step), which preserves tone and emotion.

GPT-Realtime-Whisper is streaming speech-to-text. Words appear as the speaker talks. Built for live captions and meeting transcription.

Integration Options

All three use the Realtime API with three connection methods:

WebRTC — browser-based, lowest latency
WebSocket — server-side, more control
SIP — telephony integration

GPT-Realtime-2: Voice Agents That Actually Work

If you've built voice agents before, you know the pain: tool calls create dead air. The user asks something that requires a database lookup, and the agent goes silent for 2-3 seconds. Feels broken.

GPT-Realtime-2 solves this with preamble — it talks through its actions while executing them. "Let me check your calendar... I see you have a meeting with Alex Kim in 12 minutes." The tool call happens in parallel with the speech.

Other developer-relevant specs:

128K context window (up from 32K)
Handles interruptions without losing context
Better instruction following for system prompts
Text tokens: $4/$16 per million (input/output)
Audio tokens: $32/$64 per million

GPT-Realtime-Translate: The $0.034/min Disruption

The translation model is priced at $0.034 per minute. For context, a human simultaneous interpreter costs $25-44 per minute.

Technical details:

Processes raw audio end-to-end (not cascaded speech-to-text-to-speech)
Preserves speaker emotion and tone
Works best with brief pauses between thoughts (labeled "turn-based" in docs)
Occasional hallucinations still occur
Supports language switching mid-stream

The end-to-end approach is what makes the quality difference. Traditional pipelines lose vocal characteristics at every stage. This model skips text entirely.

GPT-Realtime-Whisper: Streaming Transcription

If you need real-time captions or meeting transcription, this is the model. Low-latency streaming output as the speaker talks.

What You Can Build

The three models together cover the full voice infrastructure stack:

Customer support agents that can reason, look up accounts, and process requests — all by voice
Real-time translation layers for international meetings at 1/1000th the cost of human interpreters
Live captioning systems for streaming, conferences, or accessibility
Multilingual voice assistants that handle code-switching naturally
Telephony bots via SIP integration that feel like talking to a person

Anthropic's Agents Now Self-Improve Between Sessions. Here's How Dreaming Works.

Evan-dong — Thu, 07 May 2026 12:12:59 +0000

On May 6th, Anthropic shipped three new capabilities for Managed Agents. Two of them — Outcomes and multi-agent orchestration — are solid infrastructure upgrades. The third one, Dreaming, is the one worth stopping to think about.

Dreaming is a scheduled background process that runs between sessions. The agent reviews its own past conversation transcripts, identifies recurring patterns, and writes learnings into its memory stores. No human prompt required. No explicit instruction to "remember this."

If you've been building with Claude agents, you already know how memory works: you tell the agent something, it stores it, it uses it next time. Passive. Explicit. You're the one deciding what gets remembered.

Dreaming flips that. The agent decides.

How It Actually Works

The process runs on a schedule between sessions. The agent scans past transcripts looking for signal: mistakes it repeated, approaches that worked, edge cases it missed. It then curates its own memory stores based on what it finds. The original session data stays untouched — Dreaming writes to memory, not back to history.

There are two autonomy modes you can configure:

Automatic: the agent identifies patterns and writes them to memory directly
Human review: the agent proposes memory updates, you approve before they take effect

The human review mode is the safer starting point for production systems. You get the cross-session pattern recognition without giving the agent unilateral write access to its own memory.

Currently in research preview — not GA yet.

Why This Matters: The Cross-Session Blind Spot

Here's the problem Dreaming solves. Individual sessions can't see cross-session patterns. A support agent that misclassifies a certain type of ticket won't notice it's made the same error 12 times this month. Each session starts fresh. The pattern is invisible.

Dreaming surfaces exactly that kind of signal. It's the difference between an agent that resets every session and one that accumulates operational experience over time.

The practical implication: an agent that's been running for three months has three months of self-curated experience. A freshly deployed agent starts from zero. Over time, these become fundamentally different systems — not because of different prompts, but because of different histories.

Outcomes: The Signal Dreaming Needs

Dreaming needs to know what "doing well" means. That's what Outcomes provides.

You define a success rubric. A separate Claude instance — isolated from the agent's reasoning, running in its own context window — evaluates output against your criteria. If it fails, the grader identifies what needs to change, and the agent iterates until it meets the bar.

Numbers from Anthropic's internal testing:

Task success rates improved up to 10 percentage points over standard prompting
Structured file generation: +8.4% on .docx, +10.1% on .pptx
Works for subjective quality — editorial voice, writing style, brand consistency

The isolation model matters here. The grader runs in a separate context window, which means it can't be influenced by the agent's own reasoning. It's evaluating output, not process.

Connect the two: Outcomes identifies failures. Dreaming remembers them. One is the exam. The other is the error notebook.

Multi-Agent Orchestration: Now in Public Beta

The third piece moved from preview to public beta. A coordinator agent decomposes tasks and delegates to up to 20 specialist subagents running in parallel. Each subagent gets its own context window. They share a common filesystem.

Key details for builders:

Full trace visibility in Claude Console
Coordinator can send follow-up messages mid-workflow
Subagents retain context between exchanges
Orchestration depth limited to one level — no sub-sub-agents

The depth limit is worth noting. If your architecture needs nested orchestration, this isn't the right fit yet.

Real-world results from early adopters:

Harvey (legal AI): task completion rates up approximately 6x
Wisedocs (document verification): review speed improved 50% while maintaining quality
Netflix: parallel batch analysis across hundreds of build logs
Spiral by Every: Haiku coordinator + Opus writing subagents + Outcomes grader scoring against editorial principles

Webhooks and Pricing

Webhooks are in public beta. Agents push notifications to your system when tasks complete. For long-running jobs — some sessions run for hours — this is essential. You don't want to poll.

Pricing: standard Claude API token rates plus $0.08 per active session hour. Idle time is free. A 30-minute task costs 4 cents in infrastructure fees on top of tokens. Dreaming, Outcomes, and Webhooks don't add separate charges.

Quick Reference

Feature	Status	What It Does
Dreaming	Research preview	Agents review past sessions, extract patterns, curate memory
Outcomes	Public beta	Automated output grading against developer-defined rubrics
Multi-agent orchestration	Public beta	Coordinator + up to 20 parallel subagents, shared filesystem
Webhooks	Public beta	Push notifications when agent tasks complete
Pricing	Live	$0.08/active session hour + standard token costs

One Limitation Worth Knowing

Managed Agents runs Claude models exclusively. The orchestration, Dreaming, Outcomes grading — all Claude. If your architecture needs to route between models (cost optimization, specialized capabilities, latency requirements), that's a layer Managed Agents doesn't address.

If you're building multi-model agent systems that need persistent context across providers, EvoLink provides a unified gateway routing across Claude, DeepSeek, GPT, and others from a single API endpoint.

Author: Jessie, COO at EvoLink

Sources:

How I Stopped Burning Through My Claude Code Quota by Noon

Evan-dong — Wed, 06 May 2026 09:58:47 +0000

By Jessie, COO at EvoLink

You open Claude Code at 9am. By noon, you're rate-limited. Your colleague does twice the work and still has quota left at 5pm. Same Max subscription. What's going on?

I ran into this exact situation and went digging. Turns out Anthropic published an internal engineering blog — "Lessons from building Claude Code: Prompt Caching is Everything" — that explains the whole thing. The short version: your daily habits are probably destroying your cache hit rate, and that's costing you 10-20x more tokens per message than necessary.

Here's what I learned and what I changed.

The Core Mechanic: Prefix Caching

Every request Claude Code sends to the model follows this structure:

System prompt + Tool definitions → Project docs (CLAUDE.md) → Session context → Messages

The API caches this sequence from the front. On the next request, if the prefix matches what was cached before, it reuses the prior computation. A cache hit costs one-tenth of normal price for those tokens.

But if any single byte in the prefix changes, everything from that point onward is invalidated. Full price recalculation.

The ordering is intentional. Anthropic's design principle: the less something changes, the earlier it goes. System prompt and tool definitions rarely change — they sit at the front. CLAUDE.md changes occasionally — middle. Messages change every turn — last. Each new turn just appends to the end. Everything before it stays cached.

Four Things That Kill Your Cache

1. Switching Models Mid-Conversation

This one hurts the most. You're mid-session with Opus, a simple task comes up, you run /model to switch to Haiku, handle it, switch back.

Cache is bound to the model. One switch = all accumulated cache invalidated, rebuilt from scratch. The rebuild cost often exceeds what letting Opus answer the simple question would have cost.

Anthropic's internal approach: keep one model for the main conversation. When a smaller model is needed, use a sub-agent — independent context and cache, does its work, passes the result back without touching the main session's cache chain.

2. Changing Tool Configuration Mid-Session

Adding an MCP tool, removing one, or updating parameters — tool definitions are part of the cached prefix. Any change breaks the chain.

This is why Claude Code keeps tool definitions in place even when unused. The cost of extra definition tokens is negligible compared to a full cache invalidation.

Plan Mode follows the same logic: instead of removing execution tools when entering planning mode, it adds EnterPlanMode/ExitPlanMode as special tools. The tool set never changes. The cache stays valid.

For users with many MCP tools, Claude Code uses lazy loading: start with lightweight stubs (tool name + one-line description), pull full schemas only when the model actually needs to call a tool.

3. Opening New Sessions Constantly

Every fresh claude invocation starts cache from zero. If your habit is "ask two questions, quit, reopen" — you never accumulate cache benefit.

4. Switching Between Accounts

Cache is isolated per account. Rotating through account pools resets the cache each time.

What to Do Instead

Keep conversations long. Longer conversation = thicker cache = cheaper messages toward the end. Stop opening new sessions unnecessarily.

You might worry about context window overflow. Don't. Claude Code has built-in compaction — automatic history compression when context gets too long. Anthropic designed Cache-Safe Forking: the compaction request reuses the exact same system prompt and tool definitions, sharing the same cache chain. The only new cost is the compression instruction itself.

Long conversations don't get more expensive. They get cheaper.

Don't switch models mid-conversation. If you need a different model, open a separate conversation for that task.

Configure MCP tools before the session starts. Don't add or remove mid-session.

Use --resume to continue previous sessions.

claude --resume

This restores your last session. The cache chain picks up where it left off. No rebuild. This single flag is probably the most underrated cost-saving habit in Claude Code.

Quick Reference

Action	Cache Impact	Cost Impact
Switch model mid-conversation	Full invalidation	Up to 20x
Add/remove MCP tools	Full invalidation	10-20x
Open new session	Start from zero	First turns at full price
Switch accounts	Full invalidation	10-20x
Long continuous conversation	Accumulates	Gets cheaper over time
Use `--resume`	Continues chain	Near-free

One More Detail Worth Knowing

Claude Code never modifies the system prompt to update state information (current time, file changes). Instead, it injects updates using <system-reminder> tags inside messages. Because modifying the prompt would break the cache. The prompt is treated as immutable infrastructure. Messages are the fluid information layer.

That's the level of obsession Anthropic has about this. They monitor cache hit rate with the same severity as server uptime. A drop is treated as an incident.

The Model-Switching Problem

"Never switch models" is painful advice in practice. Sonnet for everyday coding, Opus for architecture decisions, Haiku for quick questions — that's a normal workflow.

Anthropic's answer is "use sub-agents," but most users can't orchestrate sub-agents themselves. If you're running Claude Code through a gateway like EvoLink, model routing can happen at the infrastructure level without breaking your session's cache chain. Worth knowing that option exists.

Caching is not an optimization technique. It is the foundation of the entire system. Now you know what Anthropic knows.

Sources:

Jessie is COO at EvoLink, a Claude API gateway for teams and developers.

Codex v0.128.0: /goal Keeps Working Until It's Done -- Even Across Sessions

Evan-dong — Sun, 03 May 2026 08:58:24 +0000

Every AI coding assistant forgets what it was doing the moment you close the terminal. Codex just fixed that.

OpenAI shipped v0.128.0 on April 30th with two features that matter more than they sound: /goal for persistent cross-session objectives, and /pet for ambient agent status feedback.

The Session Amnesia Problem

You ask your AI assistant to refactor a module. It gets halfway through. You close the terminal, grab coffee, come back -- and it has zero memory of what it was doing.

You re-explain the task. It starts over. You lose 15 minutes of context every single time.

This is the intent persistence problem. Not context window size -- the model simply forgets your objective when the session ends.

/goal: Define It Once, Codex Keeps Going

/goal lets you set a persistent objective that survives across sessions:

/goal create "Increase test coverage in src/auth/ from 62% to 90%"

Close the terminal. Reboot. Come back tomorrow. The goal is still there.

Command	What it does
`/goal create`	Define a persistent objective
`/goal pause`	Suspend the goal, preserve progress
`/goal resume`	Pick up where you left off
`/goal clear`	Mark done or abandon

Under the hood, goal state is managed through app-server APIs with runtime continuation. When you /goal resume, Codex restores the execution context -- not just the goal text.

This shifts AI coding from request-response to goal-driven agent: you define the destination, the tool figures out how to get there across as many sessions as it takes.

/pet: Agent Observability, But Cute

Type /pet and a small animated creature appears in your Codex interface. It reflects what Codex is doing in the background:

Running a task? The pet is active.
Tests passed? It celebrates.
Something stuck? It reacts.
Idle? It sleeps.

9to5Mac called them "little Dynamic Island-ish messengers." Sam Altman said: "This isn't the most important thing we've done, but it's more useful than it looks."

You can also /hatch a custom pet -- Codex generates one based on your project context.

Silly? Sure. But agent observability during long-running tasks is a real problem, and this solves it without requiring you to tail logs.

What This Signals

When Cursor, Claude Code, and Codex generate roughly similar code, what differentiates them?

Dimension	Old	New
Task scope	Single-turn	Multi-session goal tracking
Agent visibility	Terminal output	Ambient status indicators
Session model	Stateless	Stateful across restarts

Once core functionality reaches parity, experience becomes the differentiator.

v0.128.0 Quick Reference

Feature	Command	Description
Virtual pet	`/pet`	Animated agent status companion
Custom pet	`/hatch`	AI-generated project-specific pet
Goal system	`/goal`	Persistent cross-session objectives
Self-update	`codex update`	Update from terminal
Side chat	`/side`	Parallel conversation panel
Plugin marketplace	marketplace	One-click plugin install

Practical Notes

Use /goal for multi-day refactors, coverage targets, migration checklists. Not for one-off fixes.
Use /pet as ambient monitoring during long agent runs.
If you are juggling multiple AI tools (Codex, Claude Code, Gemini), the fragmentation tax is real. EvoLink unifies 30+ models behind one API gateway with smart routing.

References:

Claude Opus 4.7: What Actually Changed and Whether You Should Migrate

Evan-dong — Thu, 30 Apr 2026 10:01:52 +0000

If you follow AI model releases, you have already seen the headlines about Claude Opus 4.7. Most of them focus on benchmark numbers.

This article focuses on something more useful: what changed in practice, what breaks during migration, and which workflows benefit most.

The Short Version

Claude Opus 4.7 is Anthropic's strongest generally available model for agentic coding and structured enterprise work as of April 2026. It is not a universal upgrade. It introduces breaking API changes that require testing before migration.

Where Opus 4.7 Is Strongest

Agentic Coding

This is the headline improvement. Anthropic describes Opus 4.7 as a notable step up over Opus 4.6 for multi-step software engineering tasks. The difference shows most on work that requires:

reading a codebase across multiple files
forming a plan and using tools
verifying outputs before finalizing
revising when initial attempts fail

If your LLM usage is mostly one-shot snippets or ad hoc brainstorming, the upgrade matters less.

High-Resolution Vision

Opus 4.7 raises the image ceiling from 1568px / 1.15MP to 2576px / 3.75MP with simpler 1:1 coordinate mapping. This matters for screenshot QA, UI bug investigation, dense chart interpretation, and document understanding workflows.

Task Budgets

A new task_budget parameter (beta) lets you give Claude an approximate token budget for the full agentic loop, including thinking, tool calls, and output. The model can prioritize work and wind down gracefully instead of hitting a wall mid-task.

Extended Thinking Control

A new xhigh effort level sits between high and max, giving finer control over reasoning depth.

What Breaks During Migration

This is the part most review posts underplay.

Sampling parameters removed. Setting temperature, top_p, or top_k to any non-default value returns a 400 error. If your production code depends on those controls, this is a migration task, not a footnote.

Extended thinking budgets removed. Adaptive thinking is now the supported path, disabled by default unless you opt in.

Thinking output hidden by default. Thinking content is omitted unless you explicitly choose a display mode like summarized. Apps that surface reasoning traces will see UX changes.

Tokenizer changed. The new tokenizer can use 1x to 1.35x more tokens depending on content. Old max_tokens assumptions and compacting logic may behave differently.

Pricing

	Input	Output
Claude Opus 4.7	$15 / 1M tokens	$75 / 1M tokens
Prompt caching write	$18.75 / 1M tokens	-
Prompt caching read	$1.50 / 1M tokens	-
Batch API	$7.50 / 1M tokens	$37.50 / 1M tokens

The headline price is simple. The real cost story is not. Because the tokenizer changed, two teams can quote the same pricing and end up with different effective costs. Replay real prompts and measure before committing.

Who Should Upgrade

Opus 4.7 is a strong fit if you are:

building coding agents that inspect, plan, and verify across files
running enterprise workflows with documents, charts, or screenshots
building long-horizon agents where follow-through matters
willing to tune effort, caching, and token budgets

Who Should Test First

Slow down if you are:

sensitive to token cost variance
dependent on sampling parameter controls
building experiences where conversational style matters more than execution
expecting a drop-in swap from Opus 4.6

Access

Available through Claude API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, and Claude consumer plans (Pro, Max, Team, Enterprise). Also rolling out in GitHub Copilot.

For teams evaluating multiple models in production, a unified API gateway like EvoLink simplifies routing and billing across providers without vendor lock-in.

Bottom Line

Claude Opus 4.7 is one of the best generally available choices for agentic coding in April 2026. Adopt it as a measured workflow decision, not as a blanket default. Test your migration path before switching production traffic.

Based on Anthropic's official launch materials and API documentation published April 16, 2026.

Why Your AI Coding Assistant Keeps Overengineering: 4 Rules That Actually Fix It

Evan-dong — Wed, 29 Apr 2026 11:00:29 +0000

You ask Claude Code to add input validation. It writes an abstract class, three subclasses, and a factory method. 200 lines. You needed 5.

You ask it to fix a bug. It refactors three adjacent functions, adds type hints everywhere, and switches all single quotes to double quotes. Half the diff has nothing to do with the bug.

This isn't a you problem. It's a fundamental LLM tendency.

Andrej Karpathy — OpenAI co-founder, former Tesla AI lead — catalogued the same frustrations. In January 2026, he posted on X about four recurring failure modes in LLM-assisted coding. Someone turned those observations into a single CLAUDE.md file. Drop it in your project root, and Claude Code follows these rules automatically.

The repo: forrestchang/andrej-karpathy-skills. Currently at 97.8k stars.

Rule 1: Think Before Coding

The most common failure mode: you say "write an export function," and the model silently decides on CSV format, all fields, overwrite existing files. You didn't specify any of that.

This rule requires the model to state assumptions explicitly, list multiple interpretations when they exist, and stop when confused rather than guessing.

Before: Silently assumes CSV, all fields, overwrites existing file.

After: "Before I implement this, I want to clarify: export format? Which fields? File handling — overwrite or append?"

One surfaces assumptions in 30 seconds. The other costs you 30 minutes of debugging.

Rule 2: Simplicity First

LLMs have a deep-seated tendency to overengineer. You ask for a discount calculation, you get a Strategy pattern with a factory method and a config file.

The rule: no features beyond what was asked, no abstractions for single-use code, no speculative future-proofing. If 200 lines could be 50, rewrite.

# Before: 40 lines of Strategy pattern
class DiscountStrategy(ABC): ...
class PercentageDiscount(DiscountStrategy): ...
class DiscountFactory: ...

# After: what was actually needed
def apply_discount(price, pct):
    return price * (1 - pct / 100)

Rule 3: Surgical Changes

You ask it to fix a bug. It fixes the bug, but also adds type hints to adjacent functions, reformats quotes, renames a variable for "clarity." Your diff is 40 lines when it should be 4.

The rule: touch only what you must. Every changed line should trace directly to the user's request. If you spot unrelated dead code, mention it — don't delete it.

# Before: asked to fix timezone bug, got 4 extra changes
def format_date(dt: datetime) -> str:  # added type hints
    "Format a datetime object for display."  # added docstring
    if dt.tzinfo is None:
        dt = dt.replace(tzinfo=timezone.utc)  # actual fix
    return dt.strftime("%Y-%m-%d %H:%M")  # changed quotes

# After: one line changed, one line in the diff
def format_date(dt):
    if dt.tzinfo is None:
        dt = dt.replace(tzinfo=timezone.utc)
    return dt.strftime('%Y-%m-%d %H:%M')

Rule 4: Goal-Driven Execution

LLMs write code and call it done. No tests, no verification.

This rule reframes vague tasks into verifiable goals: "Add validation" becomes "Write tests for invalid inputs, then make them pass." The model loops until criteria are met instead of stopping at "looks about right."

Installation

# Claude Code plugin
/plugin marketplace add forrestchang/andrej-karpathy-skills
/plugin install andrej-karpathy-skills@karpathy-skills

# Or direct download
curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md

My Take

Of the four, Surgical Changes delivers the biggest day-to-day improvement. Overengineering is obvious — you see 200 lines and know something's wrong. Bad assumptions surface during debugging. But drive-by changes to unrelated code slip through review, especially in large diffs where formatting changes mix with logic changes.

Limitation worth noting: these rules address behavioral tendencies, not capability gaps. If the model doesn't understand your architecture, four rules won't save it.

One file, zero setup, immediate effect. Worth the 30 seconds.

If you're building AI workflows that call multiple models, EvoLink provides a single API gateway to 30+ models — one endpoint, pay-per-use, no vendor lock-in.

How to Use HappyHorse 1.0 API: 4 Endpoints, 6 Prompt Templates, and Real Pricing

Evan-dong — Mon, 27 Apr 2026 12:26:49 +0000

If you've been waiting for an AI video generation API that's actually production-ready and not just a demo page, HappyHorse 1.0 just shipped.

Alibaba released public API access to HappyHorse 1.0 on April 27, 2026. It's the model that topped Video Arena's blind testing rankings, and it comes with four distinct endpoints covering text-to-video, image-to-video, reference-based generation, and natural language video editing.

Here's what you need to know to start building with it today.

The Four Endpoints

happyhorse-1.0-t2v (Text-to-Video)

Pure text prompt to video. No reference images needed. This is your starting point for most creative generation tasks.

Pricing: 720P at 0.9 RMB/second, 1080P at 1.6 RMB/second.

happyhorse-1.0-i2v (Image-to-Video)

Feed it a static image plus a text prompt, and it animates the image with natural motion and camera movement. Strong visual consistency with the source.

Pricing: Same as t2v.

happyhorse-1.0-r2v (Reference-to-Video)

The consistency powerhouse. Supports up to 9 reference images for subject and scene stability across shots. Use this when you need character consistency or precise creative control.

Pricing: Same tier.

happyhorse-1.0-video-edit (Video Editing)

Natural language video editing. Modify existing videos using text instructions and up to 5 reference images. Handles both local and global edits while preserving original motion dynamics.

Pricing: Same tier.

6 Prompt Templates That Actually Work

The difference between mediocre and cinematic output comes down to prompting technique. Here are six patterns I'd recommend starting with.

1. Establishing Shot with Camera Push

An elderly fisherman in a deep blue wool sweater stands at the edge of a stone pier, mending fishing nets. Dusk settles over the bay, a lighthouse visible in the distance. Camera begins wide, then slowly pushes in to medium shot, focusing on his weathered hands. Seagulls circle overhead. Hyperrealistic, cinematic.

Start wide, narrow in. Explicit camera direction creates tension.

2. Multi-Shot Action Sequence

Shot 1: Side tracking shot, dirt bike rider launches off earthen ramp, slow motion. Shot 2: Low angle, motorcycle clears rusted school bus, sun behind rider. Shot 3: Landing, suspension compresses, mud splashes toward camera. Gritty texture, sun-bleached color grade.

Number each shot explicitly. HappyHorse transitions between them automatically within a single generation.

3. Portrait with Micro-Movements

Close-up, woman with copper hair and freckles, direct eye contact. Soft window light from the left. She blinks, corner of her mouth lifts slightly, an autumn leaf drifts past her cheek. Shallow depth of field, subtle film grain.

For portraits, specify micro-movements. The model excels at controlled, subtle motion.

4. Anime Style

Anime style. Female student in navy uniform on rooftop at sunset, wind lifting her hair. Camera orbits from rear view to profile. Cherry blossom petals drift across frame. Soft cel-shaded colors, crisp linework.

Lead with style declaration. The model maintains 2D aesthetic consistency without collapsing into 3D.

5. Product Commercial

15-second product ad. Shot 1: Extreme close-up, water droplets on matte black running shoe, slow motion. Shot 2: Runner on wet urban street at dawn, side tracking. Shot 3: Product hero shot, shoe rotates on white pedestal, soft rim lighting. Clean, premium aesthetic.

Commercial structure: detail, action, hero. Abstract direction like "premium aesthetic" translates well.

6. Environmental Atmosphere

Vast salt flat, blue hour, cracked white surface to flat horizon. Single figure walks toward camera from 100 meters, silhouetted against sunset. Wind kicks up dust haze. Camera low angle, static. Cool cyan shadows, warm coral at horizon. Hyperrealistic, anamorphic widescreen.

When environment is the protagonist, establish the world first, then introduce the figure.

Image-to-Video: 6 Techniques

Describe action, not the image. The model sees your reference. Write only what happens next.
Use clean source images. Sharp focus, good lighting. The model preserves detail but inherits defects.
Pre-crop to target aspect ratio. 16:9, 9:16, or 1:1 before upload.
Specify camera language. "Slow push in" beats "make it move."
Use I2V for character consistency. Generate the still in an image model first, then animate.
Shorter duration = greater stability. 5 seconds is the sweet spot for living photo results.

Getting Started

HappyHorse 1.0 is available through EvoLink, providing API access for developers and creators worldwide.

Video Arena's top-ranked model is now production-ready. If you've been building with video generation APIs and hitting quality ceilings, this is worth evaluating.

What video generation use cases are you working on? Drop a comment below.

DeepSeek V4 Flash vs Pro: How to Choose the Right Route for Your Coding Stack

Evan-dong — Sat, 25 Apr 2026 12:44:28 +0000

If your team is evaluating DeepSeek V4 right now, the most useful question is not "should we use it?" — it's "which tier, and for which workloads?"

As of April 24, 2026, DeepSeek's API now officially lists deepseek-v4-flash and deepseek-v4-pro with published pricing, 1M context, and 384K max output. Reuters separately confirmed the preview launch on the same date. The model is usable now, but preview status means you should still treat behavior as subject to change.

This guide is for engineering leads and platform teams who need to make a concrete routing decision — not a launch recap.

Who this is for

Platform teams migrating away from deepseek-chat and deepseek-reasoner before the July 24, 2026 deprecation
Engineering leads deciding where Flash fits vs. where Pro earns its cost
Teams trying to lower coding-model spend without replacing their premium fallback routes

Flash vs Pro: the one-paragraph decision

Flash (deepseek-v4-flash): $0.14 input / $0.28 output per 1M tokens. Use this as your default route for code generation, repo reading, summarization, and agent loops where throughput matters. The compatibility aliases (deepseek-chat, deepseek-reasoner) map to Flash behavior on deprecation, so it's also the lowest-risk migration target.

Pro (deepseek-v4-pro): $1.74 input / $3.48 output per 1M tokens. Use this as your escalation route for harder reasoning, multi-step analysis, and coding tasks where Flash doesn't clear your quality bar.

The mental model that works best in production: Flash = default, Pro = escalation. Don't flip everything to Pro by default.

Real cost shape by workload

These are rough estimates using official public pricing to show the cost difference at scale — not guaranteed production numbers.

Scenario 1: Repository analysis (250K input / 20K output)

Model	Estimated cost
DeepSeek V4 Flash	~$0.05
DeepSeek V4 Pro	~$0.51
GPT-5.4	~$0.93
Claude Opus 4.7	~$1.75

Flash is the obvious first test for codebase reading, dependency audits, and repo summarization.

Scenario 2: Multi-turn coding agent (120K input / 80K output)

Model	Estimated cost
DeepSeek V4 Flash	~$0.04
DeepSeek V4 Pro	~$0.49
GPT-5.4	~$1.50
Claude Opus 4.7	~$2.60

Output-heavy workloads punish expensive output pricing hard. This is where Flash's $0.28/M output rate matters most.

Scenario 3: Long document review (400K input / 25K output)

DeepSeek still holds a major cost advantage here. GPT-5.4 also documents a long-context premium rule (2x input / 1.5x output) for prompts above 272K tokens, which can change the economics significantly for large-context sessions.

Migration checklist: from deepseek-chat / deepseek-reasoner

DeepSeek's official docs confirm both legacy names are deprecated on July 24, 2026 and map to Flash compatibility behavior. Here's a practical migration path:

Inventory every current reference to deepseek-chat and deepseek-reasoner in your codebase
Test Flash first — because the compatibility aliases map to Flash, it's the lowest-risk first step
Promote only specific workloads to Pro — give Pro a narrow job (difficult coding, deeper analysis) before expanding its scope
Keep rollback routes active — preview means you should be able to revert quickly if quality, latency, or schema behavior changes

Where DeepSeek V4 has real limits

Preview status still matters. Reuters explicitly describes the release as a preview. Behavior can still change before finalization.

You still need your own eval set. No benchmark page tells you whether a model handles your specific codebase, your prompts, your failure patterns, and your latency budget — especially for agent loops, diff quality, and schema reliability.

Premium closed models still win on some tasks. Claude Opus 4.7 and GPT-5.4 are not going away for:

Highest-risk code changes
Hardest agentic tasks
Enterprise workflows where failure costs are high

When to keep Claude Opus 4.7 or GPT-5.4

Keep Claude Opus 4.7 if your team handles the hardest coding and review tasks and agent reliability matters more than token cost. Anthropic confirmed Opus 4.7 is generally available at $5/M input, $25/M output — same as Opus 4.6.

Keep GPT-5.4 if your team is already deeply invested in the OpenAI platform and your workflow depends on surrounding tooling as much as the model itself.

The stack that works for most teams

DeepSeek V4 Flash  →  default routing (code gen, repo reading, agent loops)
DeepSeek V4 Pro    →  escalation (harder reasoning, complex coding tasks)
Claude Opus 4.7    →  premium fallback (highest-stakes work)
GPT-5.4            →  premium fallback (OpenAI platform-dependent work)

This is usually better than trying to crown one universal winner.

Production rollout checklist

Define 20–50 real tasks from your own workload
Separate simple default-route tasks from premium-route tasks
Benchmark Flash and Pro independently
Compare output quality, not just benchmark headlines
Measure cost per successful task, not just cost per token
Keep rollback routes for GPT-5.4 or Claude Opus 4.7
Version prompts and evaluation harnesses
Log tool-call failures and schema failures separately
Watch latency and retry patterns during preview
Decide in advance what counts as "good enough to promote"

Sources: DeepSeek API Docs, DeepSeek Pricing, Anthropic Claude Opus 4.7, OpenAI GPT-5.4, Reuters

Tags: #deepseek #api #llm #aiengineering #codingtoolss

DeepSeek-V4 Runs on Huawei Ascend Chips at 85% Utilization — Here's What That Means for AI Infrastructure and Pricing

Evan-dong — Fri, 24 Apr 2026 08:38:42 +0000

DeepSeek released V4 on April 24, 2026. The headline numbers are striking on their own: 1 million token context window, Agent capabilities rivaling Claude Opus 4.6 on non-reasoning tasks, and API pricing 90% cheaper than GPT-4 Turbo. But the real story is what's underneath — DeepSeek-V4 runs on Huawei Ascend chips with 85%+ utilization, proving that China's domestic AI hardware stack can now compete with, and potentially undercut, Western alternatives built on Nvidia GPUs.

This isn't just a model release. It's a strategic signal about the future of AI infrastructure.

The Huawei Ascend Partnership: From "Usable" to "Competitive"

DeepSeek-V4 is the first Tier-1 large language model to achieve full inference compatibility with Huawei Ascend chips, with reported utilization rates exceeding 85%. For context, most domestic Chinese AI chips have struggled to hit 60% utilization on production inference workloads due to software stack immaturity and operator coverage gaps.

What changed to make 85% utilization possible:

1. Deep Hardware-Software Co-Optimization

DeepSeek worked directly with Huawei to optimize kernel implementations for Ascend 910B and Ascend 950 chips, focusing specifically on the operations that define V4's architecture:

MoE (Mixture of Experts) routing: The sparse activation pattern that lets V4 use only a fraction of its 1.6 trillion parameters per inference call
Sparse attention computation: The DSA mechanism that compresses attention at the token dimension
Memory-intensive operations: The Engram architecture's retrieval module that bridges CPU and GPU memory

2. Custom Operator Fusion for CANN Framework

Traditional Transformer operations were re-engineered to align with Huawei's CANN (Compute Architecture for Neural Networks) framework. Standard deep learning operators designed for CUDA had to be decomposed and reassembled to match Ascend's compute graph execution model. This eliminated memory bandwidth bottlenecks that previously capped utilization at ~60%.

3. Production-Scale Validation

DeepSeek's internal engineering teams have been running V4 on Ascend infrastructure for weeks before the public release. Their reported findings:

Inference quality matches Nvidia A100 deployments across standard benchmarks
Hardware costs reduced by approximately 40% compared to equivalent A100 clusters
Throughput scales linearly up to the cluster sizes tested

Why this matters for the broader AI industry:

Since the U.S. imposed high-end GPU export restrictions on China in October 2022, Chinese AI labs have been forced to choose between three options:

Stockpile pre-ban Nvidia chips — finite supply, increasingly expensive on secondary markets
Use older or smuggled GPUs — legal risk, limited performance ceiling
Wait for domestic chip alternatives to mature — capability gap, uncertain timeline

DeepSeek-V4 proves that option 3 is now viable at production scale. If a model can match Claude Opus 4.6 on non-reasoning tasks while running entirely on domestic Chinese hardware, the "you need Nvidia to compete in AI" narrative starts to crack.

The Pricing Bomb: V4-Flash at $0.014 Per Million Input Tokens

DeepSeek-V4 introduces tiered pricing across two model sizes, both with the full 1 million token context window:

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context Window
DeepSeek V4-Pro	$0.55	$2.19	1M tokens
DeepSeek V4-Flash	$0.014	$0.28	1M tokens

For comparison, here's what you'd pay with competing Western models:

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context Window
GPT-4 Turbo (OpenAI)	$10.00	$30.00	128K tokens
Claude Opus 4.6 (Anthropic)	$15.00	$75.00	200K tokens
Gemini 3.1 Pro (Google)	$1.25	$5.00	2M tokens
DeepSeek V4-Flash	$0.014	$0.28	1M tokens

V4-Flash is 700x cheaper than GPT-4 Turbo on input tokens, and 100x cheaper on output tokens.

Even V4-Pro — the flagship model with Agent capabilities approaching Claude Opus 4.6 — costs $2.19 per million output tokens compared to Opus's $75. That's a 34x price difference for comparable non-reasoning performance.

What You Can Actually Build at These Prices

Scenario 1: Long-context document analysis

Process a 500-page legal contract (~200K tokens input, ~10K tokens output):

GPT-4 Turbo: $2.00 (input) + $0.30 (output) = $2.30 per document
DeepSeek V4-Pro: $0.11 (input) + $0.02 (output) = $0.13 per document
DeepSeek V4-Flash: $0.003 (input) + $0.003 (output) = $0.006 per document

At V4-Flash prices, you could analyze 383 legal contracts for the cost of analyzing one on GPT-4 Turbo.

Scenario 2: Agent-based coding assistant

Generate 50K tokens of code per day for a development team (1.5M output tokens/month):

Claude Opus 4.6: $112.50/month
DeepSeek V4-Pro: $3.29/month
DeepSeek V4-Flash: $0.42/month

Scenario 3: High-volume customer support chatbot

Serve 1 million user queries per month (average 1K input tokens + 500 output tokens per query):

GPT-4 Turbo: $10,000 (input) + $15,000 (output) = $25,000/month
Claude Opus 4.6: $15,000 (input) + $37,500 (output) = $52,500/month
DeepSeek V4-Flash: $14 (input) + $140 (output) = $154/month

At these price points, entire categories of AI applications — enterprise document processing, automated customer support, code generation pipelines, research summarization — become economically viable for small teams and individual developers who previously couldn't afford production-scale LLM deployments.

Technical Foundations: The Three Architectural Innovations Behind V4's Cost Structure

DeepSeek didn't just slash prices by running on cheaper hardware. V4 introduces three architectural innovations that fundamentally reduce the cost of inference at every level of the stack.

Innovation 1: Engram Architecture — Separating Memory from Computation

Traditional Transformer models store all learned knowledge in GPU memory through their parameter weights. This creates a direct coupling: longer context windows and larger knowledge bases require proportionally more expensive GPU memory.

V4's Engram architecture breaks this coupling by splitting the model into two distinct modules:

Static knowledge retrieval module: Stores factual knowledge, world knowledge, and learned patterns in cheap CPU RAM using a hash-based lookup mechanism. This module handles the "what does the model know" question.
Dynamic reasoning module: Runs on GPU and handles the "how should the model think about this specific query" question. It decides which memories to retrieve from the static module and integrates them into the inference chain.

The practical result: V4 can handle 1 million token context windows without proportional GPU memory growth. This is why DeepSeek can offer 1M context as the default for all API tiers — the marginal cost of extending context from 128K to 1M is minimal because the expensive GPU memory isn't what scales.

This is a fundamentally different approach from OpenAI's and Anthropic's architectures, which still couple knowledge storage and reasoning computation in the same GPU memory space.

Innovation 2: mHC (Manifold-Constrained Hyper-Connections) — Stable Deep Network Training

Training a 1.6 trillion parameter Mixture of Experts model is notoriously unstable. Gradients explode, training runs collapse, and teams waste weeks of compute on failed experiments. This instability is one of the hidden costs that inflates the price of frontier models.

V4 uses mHC (Manifold-Constrained Hyper-Connections) technology to solve this:

Layer connections are projected onto a bi-stochastic matrix manifold using the Sinkhorn-Knopp algorithm
This enforces a mathematical invariant: signal conservation — the sum of inputs equals the sum of outputs at every node in the network
The constraint prevents the "signal explosion" phenomenon that normally kills deep network training runs

The practical result: DeepSeek can train deeper, more parameter-efficient models without the trial-and-error waste that inflates training costs at other labs. Fewer failed training runs = lower amortized cost per inference = lower API prices.

Innovation 3: DSA (DeepSeek Sparse Attention) — Token-Level Compression

Standard attention mechanisms compute pairwise relationships between all tokens in the context window, creating O(n²) computational complexity. This is why long-context inference is expensive — doubling the context length quadruples the attention computation.

V4's DSA (DeepSeek Sparse Attention) compresses attention computation at the token dimension, not just the head dimension (which is what most prior sparse attention methods target). Combined with learned sparse attention patterns, this achieves:

Compute reduction from O(n²) to near-linear scaling
60-70% reduction in memory bandwidth requirements
1M token context inference on consumer-grade hardware (for the Flash tier)

The practical result: Lower inference compute per token → lower electricity and hardware costs per API call → lower API prices passed to developers.

The Geopolitical Subtext: A Deliberate Mirror Image

On April 23, 2026 — one day before V4's public release — Reuters reported that DeepSeek refused to grant early API access to U.S. chip manufacturers, including Nvidia. This mirrors the U.S. government's October 2022 ban on exporting high-end AI GPUs (A100, H100) to China.

The strategic sequence:

U.S. restricts chip exports to China → Chinese AI labs lose access to H100/A100 GPUs
DeepSeek builds V4 on Huawei Ascend → proves domestic Chinese chips can run Tier-1 models at production scale
DeepSeek restricts U.S. access to V4 API → signals technological parity and strategic independence

This isn't just about one model or one company. It's about ecosystem decoupling:

If Chinese labs can train and deploy competitive models on domestic hardware...
And Chinese cloud providers (Alibaba Cloud, Tencent Cloud, Huawei Cloud) offer these models at 1/100th the price of Western alternatives...
Then the global AI supply chain splits into two parallel technology stacks: one built on Nvidia/CUDA/AWS/OpenAI, one built on Ascend/CANN/Huawei Cloud/DeepSeek.

For developers and enterprises, this creates a new dimension of technology strategy that didn't exist 12 months ago.

What DeepSeek-V4 Means for Developers Outside China

Short-Term Impact (2026-2027)

Price pressure on Western AI providers: If DeepSeek can offer GPT-4-class models at $0.28/M output tokens, OpenAI and Anthropic will face margin compression. Expect aggressive price cuts or new "economy" model tiers from Western providers within 6 months.
Multi-model routing becomes standard architecture: Developers will route simple classification, extraction, and summarization tasks to V4-Flash ($0.28/M) while reserving complex reasoning, safety-critical, and creative tasks for Claude Opus 4.6 ($75/M) or GPT-4 Turbo ($30/M). The cost difference makes single-model architectures economically irrational.
Geopolitical compliance becomes a development concern: U.S. developers may face restrictions on using Chinese AI APIs, similar to TikTok-related concerns. Enterprise compliance teams will need to audit model provenance and data routing.

Long-Term Impact (2028+)

Two parallel AI ecosystems: Western stack (Nvidia + OpenAI/Anthropic/Google) vs. Chinese stack (Ascend + DeepSeek/Alibaba/Baidu). Developers building for global markets may need to maintain dual implementations.
Commoditization of intelligence: If 1M-context models cost $0.28/M tokens, AI becomes infrastructure — like cloud storage, CDN bandwidth, or database queries. The competitive moat shifts from "access to intelligence" to "what you build with intelligence."
Open-source ecosystem fragmentation: DeepSeek releases model weights, but they're optimized for Ascend chips. Western researchers may struggle to replicate results on Nvidia hardware without significant re-optimization, fragmenting the open-source AI community along hardware lines.

How to Access DeepSeek-V4: API Reference and Quick Start

REST API

curl https://api.deepseek.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum entanglement in simple terms"}
    ],
    "max_tokens": 1000,
    "temperature": 0.7
  }'

Model Options

deepseek-v4-pro — Flagship model, optimized for Agent workflows and complex multi-step tasks
deepseek-v4-flash — Faster inference, lower cost, retains 98% of Pro's reasoning ability

Reasoning Mode for Complex Agent Tasks

{
  "model": "deepseek-v4-pro",
  "reasoning_mode": true,
  "reasoning_effort": "max",
  "messages": [
    {"role": "user", "content": "Design a microservices architecture for a real-time bidding system"}
  ]
}

Reasoning mode activates chain-of-thought inference similar to Claude Opus 4.6's extended thinking mode. Use reasoning_effort: "max" for complex architectural decisions, code generation, and multi-step problem solving.

Open-Source Model Weights

Hugging Face: huggingface.co/deepseek-ai/DeepSeek-V4-Pro
ModelScope (China): modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro

Quick Start

Try DeepSeek-V4 directly: DeepSeek Chat on EvoLink

The Bigger Picture: Post-Scaling Law AI

DeepSeek-V4 represents a paradigm shift from brute-force scaling to architectural efficiency:

Old paradigm: More parameters + more training data + more compute = better models. This is the approach that drove GPT-3 → GPT-4 improvements.
New paradigm: Smarter architectures (Engram) + memory-compute separation + sparse attention (DSA) + training stability (mHC) = cheaper, more capable models on diverse hardware.

This matters because:

Scaling returns are diminishing: The improvement from GPT-4 to GPT-5 is marginal compared to GPT-3 to GPT-4. The low-hanging fruit of pure scale is gone.
Efficiency becomes the competitive moat: If you can deliver GPT-4-class intelligence at 1/100th the cost, you don't need to be 10x smarter — you just need to be 10x cheaper. DeepSeek is betting on this strategy.
Hardware diversity wins: When models are optimized for architectural efficiency rather than raw compute, they can run on diverse hardware platforms — Huawei Ascend, AMD Instinct, Intel Gaudi, even mobile chips. Nvidia's GPU monopoly weakens as the industry moves from "more FLOPS" to "smarter FLOPS."

DeepSeek-V4 is the first major model to prove this thesis at production scale.

Final Thoughts

The question DeepSeek-V4 poses isn't "is it better than Claude or GPT-4 on benchmark X?" The question is: what happens to the AI industry when intelligence costs $0.28 per million tokens?

We're about to find out.

Resources:

Disclosure: This analysis is based on publicly available information and technical documentation. The author has no financial relationship with DeepSeek, Huawei, or competing AI providers.

GPT Image 2 + Seedance 2.0: A Practical Workflow from Static Visuals to Publishable Shorts

Evan-dong — Thu, 23 Apr 2026 08:17:13 +0000

If you've been working with AI visuals lately, you've probably felt a clear shift: image generation and video generation are no longer two disconnected steps. They're becoming a reusable production pipeline.

The core idea is simple: use GPT Image 2 to design the visuals correctly first, then use Seedance 2.0 to turn those visuals into motion, rhythm, atmosphere, and sound.

Why this division of labor works

A lot of people start by throwing a single text-to-video prompt at a model and hoping the result will feel cinematic. Sometimes the video moves, but the storytelling collapses. Sometimes the cuts are interesting, but the character design drifts.

The more reliable approach is to divide the work properly:

GPT Image 2 handles pre-production visual design: character sheets, storyboard grids, comic pages, posters, title cards, key art
Seedance 2.0 handles motion and audiovisual execution: camera movement, shot progression, sound atmosphere, final video feel

When you first lock the character, framing, and visual order with GPT Image 2, then pass the result into Seedance 2.0, you're breaking one difficult task into two more manageable ones.

Workflow 1: Storyboard grid → 15-second trailer

Generate a 3×3 storyboard grid with GPT Image 2 where each panel represents a shot, then use that image as the starting frame for Seedance 2.0 and guide the sequence with a shot-by-shot motion prompt.

This works because:

Pacing is naturally controlled — each panel already corresponds to a defined beat
Character and style consistency are stronger — all nine shots are generated inside one unified image
Seedance 2.0 is far more likely to interpret the input as a multi-shot sequence

Workflow 2: Comic page or character sheet → animated short

Treat GPT Image 2 outputs — comic pages, character sheets, narrative design boards — as visual scripts, then use Seedance 2.0 to animate them.

The condition is simple: the input image must not only be beautiful; it must be usable as shot design.

The practical sequence

Step 1: Write shot intent before you write prompts

Before generating anything, write a short shot list. Even for a 15-second piece, define the opening beat, middle beat, escalation, and ending hold.

Step 2: Generate the storyboard or character sheet with GPT Image 2

Use a structured prompt that specifies panel count, shot types, and visual style. The goal is not a pretty image — it's a usable production asset.

Step 3: Pass the image into Seedance 2.0 with a motion prompt

Reference specific panels in your motion prompt. Describe camera movement, pacing, and transitions explicitly.

Step 4: Iterate on the motion prompt, not the image

If the video doesn't feel right, adjust the motion prompt first. Only regenerate the source image if the visual design itself is the problem.

Prompt resources

For ready-to-use GPT Image 2 prompts covering storyboard grids, character sheets, comic pages, and more:

EvoLinkAI/awesome-gpt-image-2-prompts

The repo includes prompts organized by use case, with notes on what works well for downstream video generation.

The most reliable path for AI trailers, animated teasers, and story-driven shorts: design the image first, then generate the video.

Google Deep Research Is No Longer a Chatbot Feature — It's a Research Platform

Evan-dong — Wed, 22 Apr 2026 11:58:59 +0000

Google's latest Deep Research upgrade is worth paying attention to, and not just because it's faster or smarter.

What changed is the product's positioning. Google is no longer presenting Deep Research as a chatbot feature that helps you look things up. With the Gemini 3.1 Pro upgrade, Deep Research Max, MCP support, multimodal grounding, and enterprise data integration, it's being positioned as a research workflow platform.

That's a meaningful distinction.

What Actually Changed

Collaborative planning: Before execution, users can now review and edit the system's research plan. This is significant — it shifts the model from "AI produces output" to "human directs workflow, AI executes."

Multi-tool support in one run: Google Search, remote MCP servers, URL Context, Code Execution, and File Search can all operate within the same research workflow.

Private data grounding: Web access can be turned off entirely, enabling research runs grounded only in internal documents. This is the enterprise unlock.

Multimodal inputs: PDFs, CSVs, images, audio, and video alongside text. Real-world research doesn't live in clean prose — product teams have slide decks, investors have filings and transcripts, operations teams have dashboards and exports.

Native visualizations: Charts and infographics generated inline. A report with structured visualizations is a business artifact that circulates internally and presents to stakeholders. That changes the product's role.

The Programmatic Layer

For developers, the interesting detail: Deep Research and Deep Research Max are available in public preview through paid tiers in the Gemini API. That opens the door for teams to build custom research products — not use Deep Research as a fixed UI, but embed its agentic capabilities into domain-specific workflows.

Specialized research applications for healthcare, legal analysis, competitive intelligence, and technical discovery become buildable primitives.

The Strategic Signal

Google's subscription positioning is telling: Deep Research sits alongside large file uploads and workflows for turning source material into blog posts, web pages, and content. The message is "productivity stack for turning information into output," not "better search."

For organizations, AI stops being an assistant and starts becoming a force multiplier for analysts, researchers, and strategy teams — when it can scan hundreds of sources, compare competing claims, synthesize against internal documents, and package the result into a usable report.

The Caveats

More capable research tooling doesn't eliminate the need for judgment. A system that produces polished, stakeholder-ready reports makes human review more important, not less. The competitive advantage won't come from using the tool. It'll come from building the review processes, source standards, and editorial discipline around it.

For unified API access to Google, OpenAI, Anthropic and 30+ models: EvoLink

DEV Community: Evan-dong

Stop Asking Claude Code for Markdown Specs. Ask for HTML Artifacts.

When HTML Beats Markdown

Example: PR Review Artifact

Example: Implementation Options

Trade-Offs

Practical Prompt

OpenAI's New Realtime Voice Models Can Think, Translate, and Transcribe — Here's What Developers Need to Know

The Three Models

Integration Options

GPT-Realtime-2: Voice Agents That Actually Work

GPT-Realtime-Translate: The $0.034/min Disruption

GPT-Realtime-Whisper: Streaming Transcription

What You Can Build

Links

Anthropic's Agents Now Self-Improve Between Sessions. Here's How Dreaming Works.

How It Actually Works

Why This Matters: The Cross-Session Blind Spot

Outcomes: The Signal Dreaming Needs

Multi-Agent Orchestration: Now in Public Beta

Webhooks and Pricing

Quick Reference

One Limitation Worth Knowing

How I Stopped Burning Through My Claude Code Quota by Noon

The Core Mechanic: Prefix Caching

Four Things That Kill Your Cache

1. Switching Models Mid-Conversation

2. Changing Tool Configuration Mid-Session

3. Opening New Sessions Constantly

4. Switching Between Accounts

What to Do Instead

Quick Reference

One More Detail Worth Knowing

The Model-Switching Problem

Codex v0.128.0: /goal Keeps Working Until It's Done -- Even Across Sessions

The Session Amnesia Problem

/goal: Define It Once, Codex Keeps Going

/pet: Agent Observability, But Cute

What This Signals

v0.128.0 Quick Reference

Practical Notes

Claude Opus 4.7: What Actually Changed and Whether You Should Migrate

The Short Version

Where Opus 4.7 Is Strongest

Agentic Coding

High-Resolution Vision

Task Budgets

Extended Thinking Control

What Breaks During Migration

Pricing

Who Should Upgrade

Who Should Test First

Access

Bottom Line

Why Your AI Coding Assistant Keeps Overengineering: 4 Rules That Actually Fix It

Rule 1: Think Before Coding

Rule 2: Simplicity First

Rule 3: Surgical Changes

Rule 4: Goal-Driven Execution

Installation

My Take

How to Use HappyHorse 1.0 API: 4 Endpoints, 6 Prompt Templates, and Real Pricing

The Four Endpoints

happyhorse-1.0-t2v (Text-to-Video)

happyhorse-1.0-i2v (Image-to-Video)

happyhorse-1.0-r2v (Reference-to-Video)

happyhorse-1.0-video-edit (Video Editing)

6 Prompt Templates That Actually Work

1. Establishing Shot with Camera Push

2. Multi-Shot Action Sequence

3. Portrait with Micro-Movements

4. Anime Style

5. Product Commercial

6. Environmental Atmosphere

Image-to-Video: 6 Techniques

Getting Started

DeepSeek V4 Flash vs Pro: How to Choose the Right Route for Your Coding Stack

Who this is for

Flash vs Pro: the one-paragraph decision

Real cost shape by workload