Coding agents ship working UI fast. They also ship the default look even faster: purple gradients, Inter heroes, three identical feature cards, and centered everything.
More mood boards alone do not fix that. What I pin for client and portfolio work is a two-layer stack: rules the agent must obey, then references that teach structure. This post is that bookmark page.
I start with two agent-native design skills. Both install as portable SKILL.md packs that Cursor, Claude Code, Codex, and peers can load.
Hallmark (Together AI / Nutlope) picks a layout family first, dresses it with a theme, then runs slop-test gates before it ships. The useful part for real work is the verb set: build by default, then study a URL or screenshot into a portable design brief, audit an existing page, or redesign with different bones.
npx skills add nutlope/hallmarkTaste Skill is the broader anti-slop pack I reach for when the brief needs a declared design direction, variance, and a hard pre-flight check. Default install is v2 design-taste-frontend, with companion skills for GPT/Codex, image-to-code, redesign audits, minimalist, and brutalist directions. Several of those variants already live in this repo under .agents/skills/.
npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend"These sit next to Hallmark or Taste Skill. They do not replace them.
baseline-ui): polish spacing, accessibility, and motion on UI you already haveSKILL.md standard that makes the packs above work across toolsWorkflow I actually use: Taste or Hallmark to set direction, ui-skills or UI Craft to clean, and never invent fake social proof or Trustpilot stars.
Skills stop the default look. Boards stop blank-page guessing. I pin sites that already convert in the category, then rebuild the information architecture in the real stack with real copy.
Landing / launch
Product / SaaS UI
Type / brand
Skills stop the default look. Boards stop blank-page guessing. Together they turn agent speed into intentional UI instead of another purple gradient landing page.
If you want help shipping a production web app with intentional UI, start on the contact page.
Moonshot AI released Kimi K3 on July 16, 2026. I am writing this the next day, with the same builder lens I used for Grok 4.5: not “who won the internet,” but what I would actually put on a long agent loop for product and agency work.
Moonshot’s own launch post says the quiet part out loud. Overall performance still trails the strongest proprietary models, Claude Fable 5 and GPT-5.6 Sol. That honesty is useful. It frames K3 as open-scale frontier competition, not a magic takeover.
Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model. Moonshot activates 16 of 896 experts per token, ships a 1,048,576-token context window, and treats vision as native input. The API model id is kimi-k3. Full weights are promised by July 27, 2026; the API and consumer products are live now.
Two product variants matter for builders:
Architecture notes from the launch materials include Kimi Delta Attention for long-context decode, Attention Residuals for training efficiency, and Stable LatentMoE. Treat the biggest speed and efficiency claims as vendor claims until the technical report ships with the weights.
Agent Swarm is the feature I care about most for real delivery work. Moonshot’s Agent Swarm docs (built through the K2 line and carried into K3 Swarm Max) describe an orchestrator plus specialist sub-agents that can run in parallel.
What the docs and launch case studies emphasize:
That shape matches how I already ship AI features: research a codebase or domain, fan out tools, then merge results into one coherent change. OpenAI’s GPT-5.6 Sol ultra mode is a closed-stack cousin (a handful of parallel subagents by default, more on some evals). K3’s bet is open scale plus swarm orchestration at lower token cost.
Independent composites put K3 right behind the two closed flagships. On Artificial Analysis Intelligence Index v4.1 around launch week:
| Model | Intelligence Index |
|---|---|
| Claude Fable 5 | 59.9 |
| GPT-5.6 Sol | 58.9 |
| Kimi K3 | 57.1 |
Moonshot’s launch table (max reasoning effort) and Arena preference boards show a sharper split by task type.
| Area | K3 | Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| Frontend Code Arena (Elo) | 1679 (#1) | 1631 | 1618 |
| Program Bench | 77.8 | 76.8 | 77.6 |
| SWE Marathon | 42.0 | 35.0 | 39.0 |
| BrowseComp | 91.2 | 88.0 | 90.4 |
| AutomationBench | 30.8 | 29.1 | 29.7 |
| Terminal-Bench 2.1 | 88.3 | 84.6 | 88.8 |
| Area | K3 | Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| DeepSWE | 67.5 | 70.0 | 73.0 |
| FrontierSWE (dominance) | 81.2 | 86.6 | 71.3 |
| HLE-Full (no tools) | 43.5 | 53.3 | 44.5 |
| HLE-Full (with tools) | 56.0 | 63.0 | 58.0 |
| GDPval-AA v2 (Elo) | 1668 | 1760 | 1748 |
| GPQA-Diamond | 93.5 | 92.6 | 94.1 |
Reading that as a builder: K3 looks strongest on long-horizon coding, web research, frontend preference, and cost-sensitive agent loops. Fable 5 still owns several hard reasoning and knowledge-work boards. Sol edges several coding and STEM rows by small margins.
API pricing at launch (cache-miss input / output, per million tokens):
| Model | Input | Output |
|---|---|---|
| Kimi K3 | $3 | $15 |
| GPT-5.6 Sol | $5 | $30 |
| Claude Fable 5 | $10 | $50 |
K3 also advertises aggressive prefix-cache hits on coding workloads, which matters more than list price once an agent is looping on the same repo context.
Membership is separate from API billing. Kimi’s consumer plans use musical tempo names (Adagio free, then Moderato, Allegretto, Allegro, Vivace). Current list prices live on the membership pricing page. Allegretto is $39/mo monthly or about $31/mo on annual billing today.
About four months ago, during the Kimi 2.5 era, I subscribed to a paid Kimi plan in that ~$30 USD range. On paper it looked like a solid mid-tier seat for long chats and agent work. In practice the product felt slow, and it often seemed like the service was hitting capacity limits. Capability on a leaderboard does not matter if you are waiting on a queue when you need to ship. That history is why I am watching K3 Swarm Max and API capacity as closely as the benchmark tables.
Kimi 2.5 also shows up inside my daily IDE stack. Cursor’s Composer 2.5 is built on the same open-source Moonshot checkpoint as Composer 2: Kimi K2.5. Cursor then runs continued pretraining and large-scale reinforcement learning on top, so Composer is not a raw Kimi wrapper, but the base lineage is explicit. Cursor’s own forum announcement repeats the same point: Composer 2.5 builds on Moonshot’s Kimi K2.5 with Cursor’s continued training (Composer 2.5 is now live). That is part of why a Kimi K3 release still matters to me even when I am mostly working in Cursor: the open base that trained Composer 2.5 just got a much larger successor.
When I would reach for Kimi K3
When I would still pick Fable 5 or GPT-5.6 Sol
Kimi K3 is the first open 3T-class model that sits within shouting distance of Claude Fable 5 and GPT-5.6 Sol on independent intelligence rankings, while leading several agentic coding and frontend preference boards at roughly half the closed flagship token cost. Agent Swarm is the practical differentiator for multi-step shipping work, not the parameter count alone.
If you are evaluating models for AI features that have to pay for themselves in a product, try the same test I use for every release: one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help shipping an AI feature or production web app with the right model for the job, start on the contact page.
Cursor and SpaceXAI released Grok 4.5 on July 8, 2026. I am writing this the next day, on the same portfolio where I just used that model to ship a real feature: the MDX blog section you are reading now.
According to the Cursor forum announcement, Grok 4.5 is Cursor’s most capable model so far, and the first one trained jointly with SpaceXAI for more than software engineering alone. It is aimed at long-running work that needs creative tool use: coding, data work, research, and other computer tasks.
Highlights from the release:
The community thread on r/cursor is where a lot of early reactions landed after the drop.
Cursor published a comparison chart across Terminal-Bench, SWE-Bench Multilingual, DeepSWE, and SWE-Bench Pro. Here is that chart:
Reading the highlighted Grok 4.5 column:
| Benchmark | Grok 4.5 | Notes |
|---|---|---|
| Terminal-Bench 2.1 | 83.3% | Nearly tied with GPT-5.5 (83.4%), ahead of Opus 4.8 (78.9%) |
| SWE-Bench Multilingual | 78.0% | Ahead of GPT-5.5 (77.8%) and Composer 2.5 (71.6%) |
| DeepSWE 1.0 | 62.0% (high) | Ahead of Opus 4.8 max (55.8%) |
| SWE-Bench Pro | 64.7% (high) | Competitive mid-pack; Fable 5 leads this row |
Benchmarks are not the whole story. What mattered for me was whether the model could stay on a multi-step Astro portfolio task without losing the IDE theme, SEO rules, and routing constraints.
I asked Cursor (running Grok 4.5) to plan an Astro blog that stayed inside this IDE chrome, not a separate marketing layout. The model researched Astro content collections, then we locked MDX so posts could embed components like callouts.
What it shipped in this repo:
src/content/blog with Zod frontmatter/blog and /blog/[slug] wired into the existing RouteId SPAblog/ folder, tab labels, and pet tipsBlogPosting schema plus llms.txt links so AI crawlers can find postsThe hard part was not “make a markdown page.” It was keeping client-side IDE navigation working while still prerendering MDX for SEO. Grok 4.5 proposed content collections for metadata, then build-time HTML templates so post bodies stay available when you switch posts without rebooting the shell.
I ran this work on Cursor Pro with the included $20 API usage bucket. By the time the blog feature and these posts were done, I had used about 6% of that quota (the dashboard sat in the single-digit range when I captured it).
Almost all of the agent work was grok-4.5-high. The usage log for that session looks like this:
Adding only the grok-4.5-high rows from that log:
| Row (approx.) | Tokens |
|---|---|
| 2.2M + 1.5M + 3.3M + 7.1M + 1.1M | 15.2M |
| 751.4K + 365.9K + 248.4K + 316.7K + 75K + 217.7K + 317.9K + 532.8K + 353.4K | ~3.2M |
| Estimated total | ~18.4M tokens |
Call it about 18 million tokens of grok-4.5-high, with a little room for rounding and any rows that scrolled off the screenshot. A few Composer rows appear in the same window, but the blog build itself was Grok 4.5 high.
For agency and product work, I care less about a single leaderboard win and more about:
That is the bar Grok 4.5 cleared on this feature. If you are evaluating models inside Cursor, try a task that spans UI, content, and SEO in one pass. That is closer to shipping than a toy prompt.
If you want help shipping an AI-assisted product feature of your own, start on the contact page.
Most stalled projects share the same pattern: unclear scope, too many tools, and no owner for launch day. A production web app needs a short path from decision to deploy.
Write one sentence for what success looks like. More leads, faster onboarding, or a store that can take payments without manual work. That sentence drives stack choices and what you cut.
Pick tools your team can host, monitor, and change. A boring stack that ships weekly beats a clever one that only the original author understands.
Document env vars, deploy steps, and who gets paged. Agencies win when the client can run the product after the build, not when the demo looks impressive for a week.
If you want help turning a brief into a live app, start on the contact page.
Business owners do not need another chatbot demo. They need features that show up in the numbers: more booked calls, fewer missed follow-ups, faster handoffs between sales and support.
Map one painful step in your funnel. Missed inbound calls, slow quote turnaround, or support tickets that repeat the same answer. That bottleneck is where AI earns its keep.
A useful first release is often a single workflow: summarize a call, draft a follow-up, or flag a risk in a transcript. Keep the UI boring. Put the intelligence where the work already happens.
Track time saved, conversion lift, or error rate. Expand only after the thin slice proves value. That is how AI projects stay funded and how agencies keep clients.
When you are ready to scope a production feature, get in touch.
portfolio / blog/building-this-blog-with-cursor-grok-4-5.mdx
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Blog post
Jul 9, 2026 · cursor, grok, ai, astro
Cursor and SpaceXAI just shipped Grok 4.5. I used it inside Cursor to plan and build the MDX blog you are reading on this IDE portfolio.
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