What I learned:

The 30-day signal is not a debate anymore - it is a division of labor. Across the last month the loud, high-engagement material on both X and Reddit is almost entirely Gaussian splatting, and almost none of it argues with NeRF at all. @gracia_vr is pushing 4D Gaussian Splatting as a creative-production tool (multi-camera volumetric capture of a dancer under strobing lights, then free viewpoint chosen in post), and drone-mapping people in r/UAVmapping are shipping splat-based orthomosaics of rooftops. The tell: when practitioners want a photoreal result on a screen, fast, they reach for splats by default and do not mention NeRF as a rival. NeRF has stopped being the thing you compare against and become the thing underneath - one resolved-entity note in the raw dump put it bluntly, that splatting is the destination-is-a-screen technology while NeRF in 2026 is 'mostly' infrastructure and research substrate.

Where NeRF still actually wins is the stuff splats are structurally bad at: physically-correct light and tiny files. The consistent finding from verification is that splats bake view-dependent appearance into the scene - lighting is captured, not modeled - so you cannot cleanly relight a splat, reshape it, or hand it to a modeller, and complex reflections off glass and mirrors break because there is no real geometry behind the virtual image. NeRF's implicit MLP representation is the friendlier starting point for inverse rendering, material decomposition, and relighting, which is exactly why the freshest arXiv work surfacing in the longxiang-ai/awesome-gaussians feed (AugSplat, StereoGS, and a run of 'Reflective / deferred-shading' Gaussian papers) is essentially splatting trying to borrow NeRF's abilities back. The other durable NeRF edge is size: NeRF scenes compress to roughly 10-50MB while a raw splat scene runs 500MB-1.5GB, which matters the moment you leave a demo GPU and try to stream to phones or the web. NeRF also degrades more gracefully in textureless or sparse-view captures where splats' structure-from-motion dependency starves.

What splatting still cannot do, even in its 2026 hot streak, is be an editable model. A splat is millions of view-dependent blobs, not a mesh with materials, so relighting, animation, physically-based reflection, transparent/thin-geometry capture, and clean handoff to a DCC modeller are all still open problems that the community is patching paper-by-paper (4D splatting for motion, deformable and scaffold variants for storage, deferred-reflection variants for glossy surfaces) rather than solving natively. The honest 2026 read is that splatting won real-time viewing and NeRF-family methods still own the physics; the fact that nobody on X is arguing about it is itself the story.

KEY PATTERNS

• Splatting owns the output, NeRF owns the substrate - the month's engagement is all splat production work (4DGS video, drone orthomosaics), with NeRF invisible precisely because it moved under the hood.
• The contrarian tell is silence - practitioners no longer benchmark splats against NeRF; they benchmark splats against photogrammetry and LiDAR, which is what 'replaced' really looks like.
• NeRF's moat is light and bytes - relighting, complex reflections, inverse rendering, and 10-50MB vs 500MB-1.5GB file size are the axes where NeRF-style implicit models still beat splats.
• Splatting's ceiling is editability - view-dependent baked lighting means no easy relight, no reshape, weak glass/mirror/thin-geometry capture, and no clean modeller handoff.
• The research delta is convergence, not competition - new arXiv splatting papers (Reflective GS, deferred-shading GS, deformable/scaffold GS, 4DGS) are splats absorbing NeRF's relighting and compactness, blurring the line the seed question assumes.

What I learned:

The 30-day delta is a document, not a vibe: on April 24 DeepSeek dropped the full DeepSeek-V4 technical report - "Towards Highly Efficient Million-Token Context Intelligence" - with actual architecture, training recipe, and eval numbers. It is the kind of paper US frontier labs stopped shipping years ago. It spells out Hybrid Attention (CSA+HCA), the Muon optimizer, a two-stage post-training pipeline, and a distillation setup that merges ten teacher models into one over 32T+ tokens. The headline flex is efficiency people can reproduce: V4-Flash reportedly runs at roughly 10% of the FLOPs and 7% of the KV cache of V3.2 at a million-token context. When a lab publishes the FLOP and KV-cache math, it is inviting you to check its work - the opposite of a model card. Meanwhile the batch coverage of Kimi K2.6 notes Moonshot shipped the open weights but did NOT disclose training data or methods, so "Chinese labs" is not one monolith - DeepSeek and Qwen publish deep, Moonshot ships weights and stays quieter on the recipe.

The most-repeated explanation this month is that openness is strategy, not idealism. The US-China Economic and Security Review Commission's March report, Two Loops: How China's Open AI Strategy Reinforces Its Industrial Dominance, frames it bluntly: China is trying to do to AI what Nvidia's CUDA did to hardware - capture developer mindshare, make Chinese models the default anyone builds on, and create dependency. Bruegel's stack-battles analysis makes the same point from the EU side: publishing detailed methods is how you set the standard others inherit. If you are structurally behind on the newest Nvidia silicon, commoditizing the model layer is a rational move - you cannot win the closed-frontier race, so you win by making the frontier free and stamping your name on it. Papers are recruiting collateral too; roughly half the world's top AI researchers are Chinese-origin, and a public research reputation is how DeepSeek and Qwen keep them.

The mirror-image explanation for why OpenAI and Anthropic went quiet is that once your model IS the product, telling people how you built it is giving away the store. The 30-day search did not surface either lab announcing "we stopped publishing" - what it surfaced is the shape of a lab optimizing for a moat. Anthropic's public output this month was safety-and-governance flavored - the Al Jazeera writeup of its call for labs to slow down - not method papers. Both labs also spent June entangled in government release restrictions, briefly pulling models to comply with a Trump directive blocking foreign-national access. That is the real asymmetry people point at: US frontier labs now operate under a national-security and commercial-moat logic that treats methods as export-sensitive IP, while Chinese labs operate under an industrial-policy logic that treats methods as market-capture infrastructure. Same secrecy question, opposite incentives.

The contrarian take worth holding onto came from X, not a think tank. @EClock24 pushed back on the celebration: "The lazy consensus in AI right now is 'open source will win.' We all say it... Look at our actions. Most of us still reach for GPT or Claude when we need something done today. We fine tune Llama on weekends and ship with OpenAI on Monday." The gap has genuinely narrowed - open-weight coding scores are within single digits of the best closed models at a tenth to a thirtieth of the cost per token - but revealed preference still leans closed for production. Publishing detailed papers wins mindshare and benchmarks; it has not yet flipped default buying behavior. On the Nemotron / NVIDIA MAD Podcast episode, Bryan Catanzaro kept circling the same open question - whether open source is "almost there or the bar keeps getting raised by the closed source models" - which is exactly the unresolved crux under all of this. And in r/ArtificialInteligence's thread on five Chinese labs cutting token prices up to 99%, the top comment was the cynic's asterisk: most of these don't offer zero-data-retention, so "you're paying with your data" - a reminder that "open and cheap" carries its own price.

KEY PATTERNS

• Publishing is a competitive weapon, not charity - DeepSeek and Qwen use detailed papers to set standards, recruit talent, and commoditize the layer where US labs make money. The USCC literally calls it the CUDA playbook.
• "Chinese labs" is not uniform - DeepSeek and Qwen publish deep architecture and training detail; Moonshot ships open weights for Kimi K2.6 but withholds the training recipe. Openness is a spectrum even within China.
• US silence tracks incentives, not cowardice - once the model is the moat and the government is gating releases on national-security grounds, methods become IP to protect rather than reputation to build.
• The gap is real but so is inertia - open-weight models now land within single digits of closed frontier on coding at a fraction of the cost, yet revealed preference (per @EClock24) still reaches for GPT/Claude in production.
• Watch the strings on "free" - the r/ArtificialInteligence crowd flagged that cheap Chinese inference often lacks data-retention guarantees, so open and low-cost can still mean paying with your data.
• Reproducibility is the tell - when a paper prints FLOP and KV-cache ratios (DeepSeek-V4), it is inviting verification; a model card that lists parameters but hides training data (Kimi K2.6) is not the same act, even from the same hemisphere.

What I learned:

The 30-day delta is that "small web" stopped being a purely nostalgic aesthetic and started producing hard numbers plus a working self-critique. The freshest concrete artifact this month is Warp Point, a retro-gaming webring launched July 4 and written up by Hackaday - not as a museum piece but as a deliberately-2026 build with modern navigation. That matters because the standing knock on webrings, documented again this cycle, is that when someone audited them in 2023 only about 20 percent could actually be navigated with the "next" button. So the movement is now shipping fixes to its own credibility problem rather than just reminiscing. Underneath that, the count people keep citing is roughly 540 active rings spanning about 22,800 member sites (single-source figure, repeated across write-ups without a clear primary census, so treat it as directional not audited).

"Small web," "IndieWeb," "web revival," "handmade web," and "personal web" are being used interchangeably for one idea: small, single-owner, single-purpose sites as an alternative to engagement-optimized platforms. Per IndieWeb.org and the movement glossaries, the through-line is ownership - you control the domain, the content, and the reading experience, versus renting attention on X, Meta, or TikTok. The most-shared framing piece this window is New Public's "The handmade internet is making a comeback" (also mirrored at danq.me), which reframes the whole thing as a "hidden creative renaissance" you have to go looking for because it is deliberately not in your feed. A parallel concept doing a lot of work is the digital garden - a personal site organized by topic and relationship rather than reverse-chronological timeline, explicitly imperfect and always in-progress, which is the anti-SaaS, anti-newsletter posture in a nutshell.

The genuinely useful signal this month is the honest disagreement about whether any of this is real, and it comes from inside the tent. The strongest reporting note: nearly every site owner interviewed wanted the resurgence to grow, but none were confident it is actually "a thing" yet. Critics inside the community push three sharp objections. One, the ad-free-paradise memory is false - the old web had banner ads, pop-ups, and tracking from early on. Two, "retroslop," the charge that revivalists blur distinct eras into a vague nostalgic sludge of "past-ness" rather than reviving anything specific. Three, and most damning, the platform-dependence contradiction: building your "independent" site on a hosted platform like Bear Blog or Neocities means you are still renting, which is the exact thing the movement claims to reject (see the Old Web Revival reality-check coverage). That self-criticism is the maturity marker - a pure fad does not audit itself this hard.

Meanwhile the "ditch SaaS" story is really two different stories wearing one headline, and conflating them is the main trap. Story one is cultural: individuals leaving social media for self-owned pages, covered as a "personal websites renaissance" driven by privacy fatigue and the fleeting nature of platform posts (WebProNews). Story two is commercial: the micro-SaaS shift, where the pitch is that users now prefer one single-purpose tool that works perfectly over a bloated platform that does ten things poorly, and solo builders dominate high-margin niches (bigideasdb on 2026 SaaS saturation). These rhyme on "small and single-purpose" but point opposite directions - one is people opting out of software economics entirely to hand-write HTML, the other is people building more software, just narrower. The engine's own comparison run underlines the scale gap: the B2B SaaS market is pegged around USD 0.49 trillion for 2026. The handmade web is a values movement measured in thousands of sites; micro-SaaS is a business strategy measured in Stripe revenue. Real growth exists in both, but "ditching SaaS for a personal page" and "ditching bloated SaaS for niche SaaS" are not the same migration.

KEY PATTERNS

• Ownership over reach: the unifying claim across IndieWeb sources is controlling your domain and reading experience, explicitly rejecting algorithmic engagement optimization.
• The movement is self-auditing: "retroslop," the platform-dependence contradiction, and the honest "none were confident it's a thing" admission are all coming from insiders, which is a maturity signal, not a death knell.
• Shipping, not just reminiscing: Warp Point this month is a built-for-2026 webring, directly answering the "only 20 percent of old webrings still work" critique.
• Two "smalls" get conflated: handmade personal sites (opting out of software) versus micro-SaaS (narrower software) share vocabulary but are opposite economic moves.
• Numbers are soft: the ~540 rings / ~22,800 sites figure is real-sounding but single-sourced and repeated without a primary census - directional evidence of growth, not proof.
• Digital gardens are the format wedge: topic-and-relationship structure over the timeline is the concrete, teachable practice pulling new owner-operators in.
• Engine caveat: this month's automated retrieval mis-resolved the entity (surfacing art-shop and tiny-home videos), so the load-bearing evidence here is the supplementary web layer, not the raw social scrape.