AI Daily Report - 2026-10-25

Opening Summary

Today’s AI news cycle tells a single, coherent story if you read it carefully: the industry has moved from building agents to governing them. Five of the day’s six top GitHub trending repositories are infrastructure for controlling AI coding agents — a security audit skill from Cloudflare (44,509 stars), an anti-”AI slop” design skill called Hallmark (32,122 stars), NVIDIA’s sandboxed agent runtime OpenShell (19,467 stars), AWS’s official MCP toolkit, and a 44,547-star Claude Code guide. Meanwhile, OpenAI reportedly announced it will stop training models on the live internet, citing “significant risks” — a decision that, if confirmed, would reshape how frontier labs source data. And Apricitas published an analysis linking 200,000 lost jobs across media, film, and the arts to AI displacement. Read together, the pattern is unambiguous: autonomous agents are now common enough that the bottleneck has shifted from capability to containment, provenance, and accountability. The tooling layer is racing to catch up with the models — and the labor market is already feeling the consequences.

🔥 Top Stories

1. The Claude Code Ecosystem Hits Escape Velocity: luongnv89/claude-howto

Source: GitHub Trending | Context: A community guide to Anthropic’s Claude Code has accumulated 44,547 stars — more than most venture-backed developer tools ever achieve — signaling that agentic coding has crossed from early adopters into mainstream professional practice.

What Happened

The repository luongnv89/claude-howto is exactly what its description says: “A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.” It is not an official Anthropic product. It is a community artifact, and that is precisely the point.

The guide’s structure reflects how the Claude Code user base has matured. Early documentation for agentic coding tools focused on installation and basic prompts. This guide reportedly spans the full arc: from installing the CLI and understanding the permission model, through structuring multi-file codebases for agent comprehension, to building “advanced agents” — subagents, hooks, custom slash commands, and MCP (Model Context Protocol) integrations. The “copy-paste templates” framing is the key differentiator: rather than abstract explanation, it ships working configurations users can drop into their own projects.

Reaching 44,547 stars places this repository in rarefied air. For context, that is roughly the scale of stars accumulated by established frameworks like FastAPI or LangChain over multiple years. A single community guide reaching this magnitude suggests an enormous, hungry user base actively searching for structured onboarding — a signal that Claude Code adoption is far broader than the vocal early-adopter cohort on X and Hacker News.

The timing matters too. Claude Code has become the reference implementation for terminal-native agentic development, and its competitors — Cursor’s agent mode, OpenAI’s Codex, Google’s Gemini CLI — have converged on similar interaction patterns. A comprehensive guide to one tool therefore functions as a de facto curriculum for the entire category.

Why It Matters 💡

Documentation is a leading indicator of market structure. When a community guide outperforms official docs in traction, it means the vendor’s surface area has outgrown its documentation team — a classic scaling problem for fast-moving platforms. More importantly, the emergence of a canonical community guide reduces onboarding friction across the entire ecosystem, which compounds adoption. Every developer who learns Claude Code through this repo is a developer who will evaluate competing tools through the same lens.

My Take 🎯

The star count here is less interesting than the category of repository that’s winning. In 2024 and 2025, the most-starred AI repos were models and frameworks. In late 2026, they’re operational knowledge — how to actually use these systems safely and effectively. That’s the hallmark of a technology transitioning from novelty to infrastructure. If you’re a developer who hasn’t built a structured agent workflow yet, this repo is a low-cost way to close the gap; the templates alone justify the click. Watch for Anthropic to either absorb this content officially or launch a certification program built on the same scaffolding.

2. Cloudflare Enters the Agent Security Business: security-audit-skill

Source: GitHub Trending | Context: Cloudflare — a company whose core business is sitting between the internet and your infrastructure — has published a “coding-agent skill” for multi-phase security audits. Its 44,509 stars in a single day indicate pent-up demand for verifiable agent security tooling.

What Happened

cloudflare/security-audit-skill is described as “a coding-agent skill for multi-phase security audits with independently verified, machine-readable findings.” Three phrases in that description carry enormous weight: multi-phase, independently verified, and machine-readable.

The “skill” format is the emerging packaging standard for agent capabilities — a bundle of instructions, tools, and evaluation criteria that a coding agent (Claude Code, Cursor, Codex) can load to gain a specialized competency. Cloudflare’s skill structures security review as a multi-phase process, which mirrors how human security auditors actually work: reconnaissance and attack-surface mapping, then targeted vulnerability analysis, then validation and reporting. Encoding that workflow as an agent skill means an AI reviewer doesn’t just scan for patterns — it follows a methodology.

The “independently verified” claim is the most consequential. Agent-generated security findings have a credibility problem: LLMs hallucinate vulnerabilities, flag false positives at high rates, and cannot be trusted to self-certify their own output. A verification layer — presumably deterministic checks, static analysis confirmation, or reproducible proof-of-concept generation — addresses the core weakness of AI security review. “Machine-readable findings” means the output plugs into CI/CD pipelines and ticketing systems rather than requiring a human to read prose.

That Cloudflare — not a security startup, but the infrastructure company that proxies a substantial share of the web — is publishing this as open tooling is strategically significant. Cloudflare’s Workers platform, its AI Gateway, and its agent-oriented products all benefit if agents operating on the internet can be audited with Cloudflare-adjacent tooling. This is ecosystem seeding.

Why It Matters 💡

Security is the gating function for agent autonomy. Enterprises will not grant coding agents write access to production repositories until they can demonstrate that agent-generated code has been reviewed to a defensible standard. A vendor-neutral, open, verifiable audit skill lowers that barrier for everyone — and positions Cloudflare as the trust layer for agentic software development. Competitors in the security tooling space (Snyk, Semgrep, GitHub Advanced Security) should read this as a shot across the bow: the audit workflow itself is being re-platformed around agents.

My Take 🎯

This is the most strategically important repo of the day, even though it’s not the most-starred. The “independently verified” clause is doing the real work — it’s an implicit admission that agent self-assessment is unreliable, and a template for how the industry will solve it: deterministic verification wrapped around probabilistic generation. My prediction: within two quarters, “verified findings” becomes table stakes for any AI security product, and unverified agent audits get treated the way unsalted password hashes are today. Teams should pilot this skill in a non-production repo now to understand its false-positive profile before trusting it in CI.

3. Hallmark and the War on “AI Slop”

Source: GitHub Trending | Context: A repo explicitly branded as an “anti-AI-slop design skill” for Claude Code, Cursor, and Codex hit 32,122 stars — evidence that the aesthetic cost of AI-generated interfaces has become a first-order product concern.

What Happened

Nutlope/hallmark is described as an “Anti-AI-slop design skill for Claude Code, Cursor, and Codex.” The term “AI slop” has entered the mainstream vocabulary over the past two years to describe the telltale output of generative models: generic gradient-purple SaaS landing pages, interchangeable hero sections, em-dash-heavy marketing copy, and interfaces that are technically competent but aesthetically anonymous. Hallmark is a skill — loaded into a coding agent — that encodes design taste as constraints.

The mechanics are worth understanding. A design skill like this typically works by injecting a set of principles, anti-patterns, and reference criteria into the agent’s context before it generates UI code. Where a naive agent produces a centered hero with a gradient and three feature cards, a “hallmarked” agent is pushed toward specificity: typographic hierarchy with intent, spacing systems, deliberate color restraint, and layouts that don’t map to the statistical median of the web. The cross-tool compatibility (Claude Code, Cursor, Codex) is notable — it implies the skill format is converging into a portable standard rather than a vendor lock-in.

The star velocity — 32,122 in a day — reflects a real pain point. Vibe-coded applications have proliferated, and their visual homogeneity has become a competitive liability. Founders shipping AI-built products are discovering that “it works” is no longer sufficient differentiation; the interface must not look machine-made. A skill that operationalizes that distinction has obvious commercial value.

Why It Matters 💡

This repo marks the beginning of a quality-control layer for generative design — the equivalent of a linter for taste. If skills like Hallmark become standard, the baseline aesthetic quality of AI-generated software rises industry-wide, which paradoxically increases the premium on genuinely distinctive human design. It also validates the “skill” as a distribution format: expect a Cambrian explosion of domain-specific quality skills (accessibility, performance, copywriting, SEO) packaged the same way.

My Take 🎯

The funniest and most important thing about Hallmark is that it’s a countermeasure against the tool it plugs into. That’s a healthy sign — the ecosystem is self-correcting. But I’d caution against treating a skill as a substitute for a designer. It raises the floor, not the ceiling. The teams that win will use Hallmark to eliminate embarrassing output, then invest human judgment where differentiation actually lives. Actionable: if you’re shipping AI-generated UI, install this and diff your output before and after. The before/after will teach you more about what “slop” means than any essay.

4. NVIDIA Builds the Cage Before the Tiger: OpenShell

Source: GitHub Trending | Context: NVIDIA released OpenShell, “the safe, private runtime for autonomous AI agents,” with 19,467 stars on day one. It’s the clearest signal yet that the agent runtime — not the model — is the next platform battleground.

What Happened

NVIDIA/OpenShell is positioned as a runtime environment — the execution layer where autonomous agents actually run — with two non-negotiable properties baked in: safety and privacy. That framing is deliberate. An agent runtime solves a problem that models alone cannot: autonomous agents need to execute code, access files, call APIs, and persist state, and every one of those capabilities is an attack surface. A runtime that sandboxes those capabilities while keeping data local addresses the two objections that block enterprise agent deployment — “what if it does something destructive?” and “what if our data leaks?”

NVIDIA’s strategic logic here is layered. The company’s core business is accelerating computation, and agent workloads — long-running, tool-calling, memory-intensive loops — are a new class of compute demand. But NVIDIA has also been building an agent story around its NIM microservices and local inference stack. A private runtime is the natural complement: it lets enterprises run agents against local models (including NVIDIA-optimized open-weight models) without routing sensitive data through third-party APIs. The “Open” in OpenShell implies open-source licensing, which is the fastest path to becoming the default runtime standard.

The 19,467 stars in a single day indicate that developers were waiting for exactly this: a credible, vendor-backed sandbox for agent execution. The competition — E2B, Modal, Daytona, and various container-based approaches — has been fragmented. NVIDIA entering with a privacy-first, open runtime could consolidate the category the way Docker consolidated containers.

Why It Matters 💡

Whoever owns the agent runtime owns the deployment surface for every agent built on top of it — and captures the telemetry, security posture, and integration points that come with it. NVIDIA is playing the same playbook it used with CUDA: provide the free, open layer that everything runs on, and monetize the compute underneath. If OpenShell becomes the default, it reinforces demand for NVIDIA hardware by making local, private agent execution frictionless. For competitors, this is a land-grab moment.

My Take 🎯

The most telling word in this repo’s description is “private.” Enterprises have been running shadow-agent pilots precisely because sanctioned runtimes didn’t exist. NVIDIA is betting that the winning runtime is the one that never phones home. My read: OpenShell will see rapid adoption in regulated industries (finance, healthcare, defense) where data residency is non-negotiable, and slower adoption among cloud-native startups already comfortable with managed sandboxes. Watch whether Anthropic, OpenAI, and Google publish compatibility statements — a runtime’s value is proportional to the agents it can host. If OpenShell supports Claude Code, Codex, and Gemini agents equally, it becomes neutral infrastructure and NVIDIA wins the layer.

5. AWS Makes Its Move: agent-toolkit-for-aws

Source: GitHub Trending | Context: AWS published official, AWS-supported MCP servers, skills, and plugins for building agents on AWS — 3,122 stars, a smaller number that belies its strategic weight. This is the cloud giant formalizing agent-native access to its platform.

What Happened

aws/agent-toolkit-for-aws is described as “Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS.” The operative word is official. Until now, agents interacting with AWS did so through community-built MCP servers of varying quality, or through brittle scripted API calls. An official toolkit means AWS is treating agents as first-class platform users — the same status it grants human developers through SDKs and the console.

The toolkit bundles three things. MCP servers expose AWS services to agents through the Model Context Protocol — the emerging standard (originally from Anthropic, now broadly adopted) for connecting models to tools and data. Skills package AWS-specific competencies in the format popularized by Claude Code. Plugins integrate the whole thing into agent frameworks. Together, they let an agent provision infrastructure, query CloudWatch metrics, manage IAM policies, or deploy Lambda functions through a supported, maintained interface rather than reverse-engineered API calls.

The “AWS-supported” designation carries real weight in enterprise procurement. It means SLAs, security review, and the implicit guarantee that the interface won’t break with the next service update. For enterprises that have been hesitant to let agents touch production cloud accounts, official support is the unlock.

The relatively modest 3,122 stars — versus 44,000+ for community repos — reflects the difference between developer enthusiasm and enterprise gravity. AWS’s audience doesn’t star repos; it files tickets and reads documentation. The strategic significance outweighs the vanity metric.

Why It Matters 💡

Cloud providers are competing to be the substrate for agentic workloads, and the interface layer — MCP servers, skills, plugins — is where that competition is fought. Microsoft (via Azure and GitHub), Google Cloud, and now AWS are all racing to publish official agent toolkits. The winner is the cloud whose services agents can operate most reliably, because agents don’t read documentation — they call APIs. If AWS’s toolkit becomes the reference implementation for cloud operations via agents, it entrenches AWS in the agentic era the way it entrenched itself in the web era.

My Take 🎯

This repo is the quiet giant of today’s list. The star count understates it by an order of magnitude. My advice: if your organization runs on AWS and is experimenting with agents, start here rather than with community MCP servers — the security and maintenance posture is categorically different. The larger trend to watch is the MCP server becoming the new SDK. Every platform will need one, and platforms without official MCP support will find themselves invisible to the agent ecosystem. Expect Google Cloud and Azure to publish near-identical announcements within weeks; the parity race is on.

6. The Creative Class Reckoning: 200,000 Jobs Lost in Media, Film, and the Arts

Source: Hacker News (Apricitas Economics) | Context: Apricitas published an analysis attributing roughly 200,000 job losses across media, film, and the arts to AI displacement. It’s the most concrete labor-market data point of the agentic era so far.

What Happened

The Apricitas piece, “AI and the Fall of the Creative Class,” quantifies what creative professionals have been reporting anecdotally for two years: approximately 200,000 jobs have disappeared across media, film, and the arts, with AI as a significant contributing factor. The analysis arrives at a moment when the debate over AI’s labor impact has been dominated by speculation and dueling projections — and it substitutes something rarer: a sector-specific accounting.

The creative industries are the leading edge of AI displacement for structural reasons. Their outputs — text, images, video, audio, code-adjacent design — map directly onto generative model capabilities. Unlike manufacturing (where automation required physical robotics) or knowledge work (where agents are still maturing), creative production has been directly exposed to models that generate competitive output at near-zero marginal cost. The 200,000 figure spans writers, editors, illustrators, production staff, and adjacent roles.

The Apricitas framing — “the fall of the creative class” — echoes Richard Florida’s famous thesis about the rise of a creative professional class as the engine of urban economic growth. The implication is that a demographic and economic pillar is being eroded. The analysis is careful to note attribution difficulty: creative employment was already under pressure from platform economics, consolidation, and the decline of legacy media business models. But the timing and magnitude of the decline, correlated with generative AI adoption curves, makes AI a primary suspect.

Why It Matters 💡

This is the first credible, sector-level quantification of AI’s labor impact, and it will be cited in policy debates for years. It reframes the conversation from “will AI take jobs?” to “which jobs, how fast, and what replaces them?” The creative sector is a canary: the same displacement dynamics will reach legal research, customer support, and junior software development as agent capabilities mature. The absence of a visible reabsorption mechanism — new roles created at the same rate as old ones destroyed — is the most alarming signal in the data.

My Take 🎯

I take the 200,000 figure seriously but the causal attribution with appropriate caution. Creative employment was already in structural decline; AI accelerated a fall that was underway. The more important question is what the next Apricitas report shows — if displacement spreads to sectors with stronger unions and better data (law, accounting, journalism’s adjacent white-collar roles), the political response will be far more aggressive than anything we’ve seen. For readers: if your work product is a text, image, or media artifact that a model can generate at 90% quality for 1% of the cost, the 10% premium you command is your entire moat. Invest in the judgment, taste, and accountability that the 90% cannot supply.

7. OpenAI Pulls the Plug on Live-Internet Training

Source: Hacker News / TechCrunch | Context: OpenAI reportedly announced it will no longer train its models on the live internet, citing “significant risks.” If confirmed, it’s a foundational shift in how frontier models are built — and a tacit admission about data contamination and provenance.

What Happened

According to TechCrunch, OpenAI said it will no longer train its AI models on the live internet, citing “significant risks.” The announcement, dated October 23, 2026, is one of the most consequential data-policy decisions any frontier lab has made.

The rationale, as reported, centers on risk. Training on the live web in 2026 means training on a web substantially generated by AI. Model collapse — the degradation that occurs when models train on their own outputs — is no longer theoretical; it is the default condition of any dataset scraped after roughly 2024. Beyond contamination, live-web training exposes models to adversarial content: prompt injection payloads embedded in web pages, poisoned data designed to install backdoors, and legally hazardous material. Each of these risks compounds as agentic systems browse the web autonomously and write their findings back into the training corpus.

Operationally, the shift implies OpenAI will rely on curated, versioned, provenance-tracked datasets — snapshots with known composition rather than a live firehose. This is a return, in spirit, to the pre-2023 era of carefully assembled corpora, but with far more sophisticated filtering and licensing. It also raises the value of licensed data deals and synthetic-data pipelines, since the live web is no longer a free input.

The Hacker News reception (3 points at time of collection) suggests the story broke quietly and may still be developing. Readers should treat the details as preliminary until OpenAI publishes a formal policy document.

Why It Matters 💡

This decision has cascading effects. First, it validates the data-licensing economy: publishers, archives, and data brokers gain leverage as live scraping is abandoned. Second, it may widen the gap between frontier labs (which can afford curated data) and open-source efforts (which often rely on web-scale scrapes like Common Crawl). Third, it changes the competitive dynamics of freshness: if models can’t learn from the live web, retrieval and tool use become even more important — models must look things up rather than know them.

My Take 🎯

I read this as OpenAI acknowledging that the web it helped fill with synthetic content is no longer a safe training input — a problem of its own making, and one the entire industry shares. The strategic bet is that curated data plus strong retrieval beats live-web training on both quality and legal defensibility. My prediction: within a year, “training data provenance” becomes a standard line item in model cards and enterprise procurement checklists. The open-source community should prepare for a harder data environment; expect a surge of investment in synthetic data generation and licensed corpus sharing.

8. The 2027 Conference Circuit: Where AI Meets HCI

Source: Hacker News (UXness) | Context: A community discussion asked which AI-in-UX/HCI conferences matter for 2027, surfacing a curated list of events. It’s a small signal of a larger shift: the HCI research community is absorbing AI as a core subject.

What Happened

A Hacker News discussion posed a simple question — “Which AI in UX or HCI conference is in your radar for 2027?” — and linked to a UXness roundup of top UX conferences for the year ahead. The low point count (3) reflects a niche audience, but the question itself is diagnostic.

The intersection of AI and human-computer interaction has become the most active frontier in design research. As agents take on more of the work of software — generating interfaces, writing copy, making design decisions — the HCI community’s questions become urgent rather than academic: How do humans maintain meaningful control over agentic systems? How should AI-generated interfaces signal their provenance? What does trust calibration look like when the system’s confidence is opaque? These are conference-track questions, and the 2027 calendar is filling with them.

The practical value of the roundup is for practitioners planning their year: which events offer substantive AI-HCI content versus which are adding “AI” to the title without changing the program. The discussion’s subtext — that attendees are actively filtering for depth over buzzwords — is itself a market signal.

Why It Matters 💡

HCI research is where the design patterns of the next decade get invented. The interaction models that make agentic systems trustworthy, legible, and controllable will emerge from this community before they reach product teams. For product leaders, tracking this conference circuit is a leading indicator of what enterprise buyers will demand in 2028.

My Take 🎯

This is a modest news item with outsized long-term relevance. The most important design question of the agentic era — how much autonomy to grant, and how to make that autonomy legible — is an HCI question, not a machine-learning one. My advice for practitioners: pick one conference with genuine AI-HCI depth and attend with a specific product problem in hand. The research community is unusually receptive to practitioner input right now, and the patterns being formalized today will become the design system defaults of tomorrow.

📊 Market & Trends

The containment layer is the new platform layer. Five of today’s six top repos — Cloudflare’s audit skill, Hallmark, NVIDIA’s OpenShell, AWS’s toolkit, and even the Claude Code guide — are about governing agents rather than making them smarter. The capability frontier has been sufficiently established; the market’s attention has shifted to safety, verification, quality, and integration. This is the classic maturation pattern: infrastructure vendors move in once the primitives stabilize.

The “skill” is becoming the dominant distribution format. Hallmark, Cloudflare’s audit skill, and AWS’s skills all use the same packaging concept: portable bundles of instructions and tools that load into any compatible agent. Cross-tool compatibility (Claude Code, Cursor, Codex) suggests skills are standardizing the way npm packages standardized JavaScript. Expect a skills registry — and a skills supply chain security problem — within two quarters.

Data provenance is becoming a competitive moat. OpenAI’s reported retreat from live-web training, combined with Cloudflare’s “independently verified” audit framing, points to a broader shift: the industry is moving from “more data” to “trustworthy data.” Licensed corpora, versioned snapshots, and verified findings become assets; scraped firehoses become liabilities.

The labor market is the lagging indicator that just caught up. The Apricitas analysis gives the creative-sector displacement a number. The tooling boom and the job losses are two sides of the same coin: agents are productive enough to displace work and common enough to require governance.

Cloud and infrastructure vendors are racing to be agent-native. AWS’s official toolkit, NVIDIA’s runtime, and Cloudflare’s security skill all represent platform companies repositioning around agents as first-class users. The MCP server is becoming the new SDK — and platforms without one risk invisibility.

🔮 Looking Ahead

Next week, watch for: formal statements from Google Cloud and Microsoft Azure on official MCP toolkits — the parity race with AWS will move fast. Also watch OpenAI’s official channels for confirmation and detail on the live-web training policy; the TechCrunch report is preliminary and the specifics (effective date, scope, synthetic-data strategy) will matter enormously.

Within a month: expect the first “skills supply chain” security incidents. A portable skill format that executes instructions inside a coding agent is a juicy attack surface, and the ecosystem has no signing or verification standard yet. Cloudflare’s “independently verified” framing may become the template for solving this.

Within a quarter: I expect at least one major enterprise to publicly standardize on an agent runtime (OpenShell or a competitor) as part of its AI governance policy, and I expect a second Apricitas-style analysis extending the displacement accounting to white-collar knowledge work.

Emerging themes to monitor: (1) the economics of curated data — who owns the post-web training corpus; (2) agent runtime consolidation — will OpenShell, E2B, or a hyperscaler win the sandbox layer; (3) HCI research translating into product patterns for trust and control; (4) labor-market policy response as displacement data accumulates.

💻 Code & Tools Spotlight

Today’s GitHub trending list is unusually actionable. Here’s how to put the top repos to work.

1. Onboard to agentic coding with the community guide:

git clone https://github.com/luongnv89/claude-howto
cd claude-howto
# Browse the templates directory — copy the subagent and hook
# configurations that match your stack into your project's
# .claude/ directory, then run:
claude
# Start with the "basic concepts" track before advanced agents.

2. Add verified security review to your agent workflow:

git clone https://github.com/cloudflare/security-audit-skill
# Load the skill into your coding agent (Claude Code, Cursor, or Codex)
# per the repo's install instructions, then invoke a multi-phase audit
# against a NON-PRODUCTION repo first to calibrate false positives.

3. Kill the slop before you ship:

git clone https://github.com/Nutlope/hallmark
# Load the design skill into Claude Code / Cursor / Codex.
# Generate a UI component before and after loading the skill,
# and diff the outputs — the comparison is the lesson.

4. Sandbox your autonomous agents:

git clone https://github.com/NVIDIA/OpenShell
# Follow the repo's setup for the private runtime, then point your
# agent's execution environment at OpenShell instead of your host.
# Verify network egress is blocked by default before granting
# filesystem or API access.

5. Give your agents official AWS access:

git clone https://github.com/aws/agent-toolkit-for-aws
# Configure the official MCP servers with scoped IAM credentials —
# never use root or admin credentials for agent access.
# Start with read-only permissions (CloudWatch, describe APIs)
# before granting any write or provisioning capability.

A closing note on the pattern: every one of these tools is a control surface — a way to constrain, verify, or standardize what an agent does. That’s the shape of the industry right now. The models are good enough; the discipline is what’s being built.

Report compiled 2026-10-25. Star counts and heat metrics reflect collection-time snapshots. The OpenAI live-web training story is based on preliminary reporting and should be verified against official announcements.

This report is based on real news collected from Hacker News, GitHub Trending, 36Kr, and Product Hunt.

Sources Referenced:

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