Techin' Geek

Nvidia has agreed to buy Hugging Face for $12.9 billion, The Information reported Wednesday night, citing a source familiar with the matter. Business Insider, which first reported over the weekend that Hugging Face was fielding takeover interest, reported Wednesday night that the talks — which would value the company at more than $13 billion — had not yet produced a signed agreement and could still atomize.

TechCrunch reached out earlier to both Nvidia and Hugging Face for comment, and neither has yet responded. (Nvidia’s silence is particularly noteworthy here as the company has moved quickly in the past to address reports it considers inaccurate.)

Maybe it was destined from the start. Hugging Face, founded in 2016, is one of the most popular hubs where developers share and download open-source AI models. Buying it would give Nvidia a strong foothold in the world of open-source AI, right as open-source developers are doing their level best to catch up to closed AI systems from companies like Anthropic and OpenAI.

Why would Nvidia want that? Most obviously, it comes down to protecting its dominance in AI chips, which, from the outside at least, appears increasingly at risk, even with Nvidia’s aggressive chip-release schedule. Pretty much all of the biggest closed-source AI labs (OpenAI, Google, Amazon, and Anthropic) are now in the process of building their own AI chips to lessen their reliance on Nvidia. A thriving ecosystem of open-source AI models gives customers more alternatives to those closed labs, which in turn keeps more of the market dependent on Nvidia’s hardware. That’s also why Nvidia has already poured tens of billions of dollars into building its own open-source AI models.

Should we be surprised that Hugging Face’s days as an independent outfit appear numbered? Not really. Hugging Face CEO Clem Delangue has spent much of this year publicly aligned with Nvidia’s open-source push, amid a debate that has been building for months, as Washington officials reportedly weighed restrictions on open-weight models. (Chinese labs like Moonshot AI had released systems — like its Kimi K3 model — that matched leading U.S. models on benchmarks while costing a lot less to run, and talk of competitive and national-security concerns grew in Washington as a result. Some critics of closed labs, like White House advisor David Sacks, suggested those fears were being fanned by the “duopoly” of Anthropic and OpenAI.)

In an appearance on CBS’s “Face the Nation” earlier this month, for example, Delangue said Hugging Face used an Nvidia-modified version of a Chinese open-source model to defend itself after a cyberattack and pointed to a recent letter — signed by Nvidia CEO Jensen Huang and 24 other companies, including Hugging Face — urging the U.S. government to support open models rather than restrict them. In a separate CNBC interview in late July, Delangue made similar points, citing that same letter while warning that China is “clearly dominating” open-source AI.

The deal would also mark something of a comeback for Nvidia in cloud computing. Nvidia reportedly scaled back its own cloud business, called DGX Cloud, about a year ago. But according to The Information, owning Hugging Face — which already helps developers run their AI models using rented computing power — could give Nvidia a way back into that market without starting from scratch.

There’s also a financial safety net at play. Nvidia has promised to help cover the cost of tens of billions of dollars in cloud computing deals for its customers. If those customers end up not using all the computing power they signed up for, Nvidia could get stuck with it. Owning Hugging Face would give Nvidia the ability to sell that unused capacity to Hugging Face’s customers.

The price marks a huge jump from Hugging Face’s last known value. The company raised $235 million in 2023 in a funding round that valued it at $4.5 billion. That round was led by Salesforce Ventures, with money also coming from Alphabet’s GV, IBM Ventures, and Nvidia itself, among others.

This wouldn’t be Hugging Face’s first brush with an Nvidia offer, either. Hugging Face turned down a $500 million investment offer from Nvidia late last year that would have valued it at $7 billion, the Financial Times previously reported. Hugging Face said at the time it didn’t want a dominant investor that could sway its decisions.

As for why it would say yes now, one could argue that a buyout is different from taking on one giant backer — a scenario that often means ceding control while being pressured to continue growing.

Hugging Face is also still a comparatively small business by revenue in the world of AI. The Information reported it was recently generating about $150 million a year in revenue, up from roughly $100 million just two months earlier.

That growth has enabled the company to get “close to profitability,” as Delangue told TechCrunch last month. Still, a price near $13 billion would be a massive multiple for a company this size and hard to resist.

Not last, the deal would give Hugging Face access to Nvidia’s much deeper pockets just as other, AI infrastructure competitors start to get pulled into other outfits, as suggested by Stripe’s recent deal to acquire OpenRouter, a startup founded in early 2023 that helps customers select different AI models to perform different tasks depending on their needs and budget.

OpenRouter was valued at just $1.3 billion back in May during its Series B round. Stripe reportedly paid more than $7 billion to make it its own earlier this month.

Amazon and Nvidia just got a lot closer. The two companies announced Wednesday an expanded partnership that includes a deal to add another 2 million Nvidia GPU chips to Amazon’s data centers.

These GPUs, which are designed to handle the heavy compute demands of training and running AI models, include Nvidia Blackwell Ultra, Rubin, and Rubin Ultra GPUs. The chips will head to Amazon Web Services’ data centers in 2027 and 2028. 

The announcement, made during Nvidia’s quarterly earnings call, comes just five months after Amazon agreed to deploy more than 1 million Nvidia GPUs across AWS infrastructure starting this year. Nvidia said in a statement that since then, “demand has exceeded those expectations.”

Neither company shared financial terms. It’s unclear what the exact return will be for Nvidia. But considering GPU units costs, the deal is worth tens of billions of dollars.

The announcement is notable not just for its size and the speed in which it grew, but also because it extends beyond Amazon buying more Nvidia chips. And it’s happening even as Amazon invests in its own potentially competing AI chips. 

Nvidia said Wednesday that its technology, including the networking hardware that connects thousands of GPUs into one system, as well as its open models, CPUs, data processing software, and robotics platform, will also be integrated across AWS. 

The companies said “surging demand” from startups, enterprises, AI labs, and even governments influenced the decision to work more closely. 

The expanded partnership comes as Amazon ramps up its own AI chip efforts — particularly with CPUs, which are the general purpose processors at the heart of servers. 

Amazon has been building its own chips to lessen its dependence on Nvidia and even compete with the chip giant. Amazon’s AI chief Peter DeSantis has said that AWS is in talks to sell its Trainium chips — which are a direct alternative to Nvidia’s H100 or Blackwell chips for deep learning workloads — to other companies for use in data centers. Amazon’s Arm-built Graviton CPU is also seen as a challenger to traditional server chips from Intel and AMD. 

Amazon has said its custom chip business is growing, noting on its last earnings call that it crossed a $25 billion annualized revenue run rate, driven by $225 billion in total commitments from AI labs like Anthropic and OpenAI. 

But, it seems Nvidia is still the GOAT in the world of AI chips. 

With the 2 million GPU chips Amazon is adding to AWS starting in the third quarter, Nvidia also plans to send an unspecified number of Vera CPUs, “some integrated with Rubin, others standalone,” according to Nvidia CFO Colette Kress. 

Nvidia CEO Jensen Huang has big plans for the company’s Vera CPUs, boasting back in May that he had found a “brand new $200 billion TAM” for the company.

Aside from AWS, Kress said Wednesday that Nvidia expects Vera to be deployed by “every major hyperscaler, neocloud, AI lab, and system OEM, with shipments already underway to our lead partners,” which include Oracle and SpaceX AI.

The partnership is also extending to Amazon’s warehouse robots and enterprise offerings. 

Kress said Amazon plans to adopt Nvidia’s full physical AI stack to power its fleet of robots. The stack includes Omniverse (its simulation and digital twin platform); Cosmos (its world model platform); Isaac (its robotics development platform); and Jetson (computing hardware for robots and edge AI). This week, Nvidia also introduced a new version of Jetson designed as a more accessible robotics computer for “entry-level edge AI.”

On the enterprise side, AWS will serve Nvidia’s Nemotron family of open models on Amazon Bedrock, its managed foundation model platform, and SageMaker, its managed cloud service. 

Nvidia also reported Wednesday that it recorded sales of $96.2 billion for the second quarter, beating analyst estimates. Data center revenue made up the majority of Nvidia’s sales for the quarter at $89 billion, up 117% from a year ago. 

Nvidia said it expects revenue to reach $108 billion in the third quarter, some of which will come from its next-gen Rubin GPUs. Nvidia said it began production shipments this quarter. Investors have been looking out for Rubin’s initial Q3 sales for signs that demand will continue into Nvidia’s next generation of hardware. 

Nvidia has committed $279 billion to secure supply and manufacturing capacity for current and future data-center projects, up substantially from $119 billion last quarter, as the chipmaker looks to secure memory and manufacturing capacity to meet AI demand over the next few years. That commitment includes $92 billion in projected spending for the rest of the fiscal year and another $87 billion in fiscal year 2028. 

“The thing that matters for the industry is that AI is now doing productive and useful work,” Huang said during Wednesday’s call. “AI is generating profitable tokens…If we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we’re at, which is the reason why everybody’s leaning in.”

Investors will be watching to see if additional compute indeed translates so neatly into additional profits as AI companies pour hundreds of billions of dollars into infrastructure. 

Google gets something right in its Wednesday announcement about new Gemini Live voice features when it says, “You shouldn’t have to guess whether a task requires Spark, a Daily Brief, or a quick inbox search.” Google means that as a promise — that the updated Gemini app can handle a variety of tasks via voice commands. But there’s a ridiculousness here: Google has given every Gemini AI feature under the sun its own branding, which undercuts that very message.

In the Gemini app, users can switch between chat, Spark, and Daily Brief — three separate features, each with its own icon and place in the app’s navigation. This clutters up what could otherwise be a more straightforward consumer experience, and it suggests that Gemini is still struggling to find a killer feature.

Take Daily Brief, for example. The feature comes across as the kind of thing an AI engineer, not an everyday user, would think is clever. It’s essentially an AI-enabled agenda that offers “proactive, personalized updates” using data pulled from Google’s apps, like Gmail and Calendar. In practice, though, the Brief can’t tell the difference between information that’s urgent or actionable and unsolicited nudges to follow up on other things — like prompting you to continue research you started in the chatbot, or worse, resurfacing your prior Google searches.

That second part doesn’t feel useful; it feels creepy. So what if I had been researching college scholarships or animal rescues on Google? That doesn’t mean I want an AI tapping me on the shoulder about them later.

Spark has the opposite problem. It’s one of the more useful aspects of Gemini’s app — an AI agent that can take action on your behalf — but Google has packaged it as its own standalone brand, which it doesn’t need to be. Sure, internally, Google engineers may want to be on the Spark team, and that’s fine — but a mainstream AI app user definitely does not need to think about which “side” of the AI app they need to be in for a given task. They should just be able to type their request, and the AI figures out how to handle it, spinning up an agent if the task calls for one.

In fairness, the problem isn’t limited to Gemini. The AI industry at large seems to expose its internal architecture directly to consumers rather than hiding it behind a simpler interface.

Today, people have to think about whether they want to “chat” with Anthropic’s Claude or “Cowork” with its help. (Until this week, those two modes inside the Claude app didn’t even share a memory of past conversations.) ChatGPT is the same, requiring you to swap between “Chat” and “Work.” This is the kind of engineering-minded design that makes engaging with AI feel unnatural. Consumers are being asked to learn the brand names for what are essentially interaction modes or surfaces, powered by a company’s AI model.

This may be why Apple’s somewhat anticlimactic approach to Siri could ultimately win over consumers. iPhone and Apple device owners don’t have to change any of their existing behavior to take advantage of it. Apple simply makes the apps and features that people already use — like Spotlight Search, the Photos app, the iPhone’s Camera, and Siri voice requests — smarter without asking users to learn a new interface.

This same principle may explain the rise of text-based AI services, where users simply text a chatbot — like Poke, Ollie, Lindy, Orchid, Lucas, Folk, Tomo, Instinct, or others — and the assistant just does what’s asked.

Text messaging is a clean and simple, well-understood user interface, and it doesn’t require extra mental effort to figure out which feature or product inside a larger app you’re supposed to use.

As a16z investment partner Justine Moore recently wrote, “People don’t want to open an app every time they need help – they want a contact they can text like a friend. And the gold standard is iMessage.”

Flipboard, the news magazine software company that has more recently invested in the open social web — the umbrella term for social platforms that let users, not one company, control their own data and audience — is making another big bet on that ecosystem. The company on Wednesday announced it’s acquiring the Portland-based feed-building startup Graze, which has been working to monetize the open social web by allowing feed creators to support themselves via ads.

Graze today offers tools to build, customize, publish, and manage Bluesky feeds, which are curated post streams that users can set as their home feed, pin for quick access, or pull into other apps built on Bluesky’s AT Protocol (an open technical standard that lets different apps share the same social network). Since launching 21 months ago, Graze has sent over 41 billion posts to some 12 million people, the company says.

The idea is that curators and communities can build their own feeds to turn their expertise into income. Most importantly, the ads are not based on the data-tracking model that relies on following individual users across the web, and which open social web supporters call the “surveillance ecosystem.” Instead, they’re contextual. That is, if a brand wants to target ads to game developers, it can buy a placement in a feed run by members of that community. What’s more, the feed’s curator can pick and choose which ads they want to run.

Meanwhile, the ad revenue is split 70/30 with Graze, with the majority of the revenue going to the feed’s creator. At present, Graze has north of 7,000 feeds on its platform, and around 60% of its traffic is monetized.

In an interview with TechCrunch, Flipboard CEO Mike McCue praised the adtech involved, saying that “it doesn’t have to surveil anybody, and it doesn’t have to violate people’s privacy…so that’s a really fantastic vision. And the key thing is that all the economics aren’t just going to one player.”

The ad revenue,” he added, “is being shared with the feed builder.”

Flipboard had been using Graze’s feed-building technology in its new app, Surf, which led to the two companies working more closely together. It turned out that the outfits shared a vision for how the open social ecosystem could grow, but they were also duplicating each other’s work.

Graze co-founder and CEO Devin Gaffney realized things could move faster with a partner that already had the infrastructure he needed — in this case, Flipboard’s existing relationships with advertisers.

Post-acquisition, Flipboard will continue to use Graze’s technology and enable its users to build feeds themselves, which will expand beyond the Bluesky app to everything running on the AT Protocol, Flipboard’s own app included.

McCue noted that in many cases, the feed builders are people who are building their own communities on the open web, but their members aren’t necessarily thinking of it in the same way.

“They don’t think of it as, like, ‘I’m joining Bluesky’ or ‘I’m joining the social web, the open social web.’ They just think I’m joining The Tea because ‘I care about women’s sports,’” he said, referencing a feed from one of the communities on Surf that’s called Bet on Her.

Flipboard isn’t disclosing the acquisition’s terms, but this was a small deal, as Graze had only raised $1 million at the beginning of 2025 and has just two team members.

Ventures Platform has raised an oversubscribed $83 million second fund as the Pan-African venture firm expands beyond its home market of Nigeria with a strategy shaped by a tougher, more selective venture market.

The firm plans to back early-stage founders across a range of sectors, including fintech, healthcare, SaaS and other areas “where technology can address essential needs and build large, enduring businesses,” Kola Aina, the firm’s founding partner, told TechCrunch. 

Of course, AI is part of that thesis.

“We’re particularly interested in where AI changes the economics of serving African markets,” he said, pointing to its potential to reduce the cost of delivering services and help overcome labor shortages. “For us, AI is most interesting when it is not simply a feature, but an enabler of an entirely different cost structure, business model or market.” 

Ventures Platform, which is headquartered in Nigeria, previously raised a $46 million Fund I in 2022 with a similar, albeit more limited scope. The first fund focused primarily on pre-seed and seed rounds.

“It allowed us to demonstrate that our approach to early-stage investing in Africa could work at an institutional scale and laid the foundation for Fund II,” Aina said. 

Now, Ventures Platform is back with a larger fund and wider geographic mandate.

The firm is expanding its focus beyond Nigeria and has already written checks from Fund II to five companies based in Kenya, South Africa, and Egypt. Check sizes will be up to $3 million, and the firm hopes to deploy the capital over the next three to four years.

“We are particularly interested in markets where technology can expand access to essential products and services, address critical infrastructure gaps, and create entirely new categories of consumption,” Aina said. 

The fundraising process took about a year and a half, with Aina describing the environment as more “selective,” than it was when Ventures Platform raised Fund I.

“LPs are asking harder questions about performance, portfolio construction, liquidity, manager discipline, and differentiation,” Aina said.

From his perspective, the market is still cautious, as LPs demand more evidence that managers can turn portfolio value into realized returns. Capital is no longer assumed to be unlimited, especially after many LPs felt burned by the venture bust a few years ago. 

“The result is a much greater appreciation for capital efficiency, stronger fundamentals, governance, regulatory engagement, and the importance of building businesses that can survive different funding cycles,” he said. “There is a much clearer understanding that building valuable companies and generating venture returns require more than simply raising successive rounds of capital.” 

This year, African startups have raised around $930 million across more than 200 deals. Last year, startups on the continent raised $1.16 billion across 447 deals.

As TechCrunch previously reported, the venture market is now a barbell — with LPs giving capital to a handful of firms at the top and to emerging managers with a track record they can trust.

“Three years ago, there was still a significant amount of curiosity around the African opportunity. Today, LPs expect proof,” Aina said, adding that this discipline is actually healthy for the market. 

“The conversation has moved from ‘Why Africa’ to ‘Why you and how exactly are you going to generate returns,’” he said, adding that simply being a pan-African fund is no longer a strategy. LPs want to know more about access to top talent, how funds are navigating individual markets, and “why you have the right to win,” Aina said. “That combination of local depth and global connectivity is increasingly important as the ecosystem matures.”

In fact, he said that is the biggest edge his firm offers. This latest generation of founders and fund managers has seen what it is like to deal with both an abundance of capital and hardly any at all. He said it’s more important than ever to understand the institutional and market realities founders face while also connecting companies to regional and global networks as they scale. 

That pitch seems to have resonated with existing investors: 70% of Fund I’s LPs returned for Fund II. Backers include the European Bank for Reconstruction and Development, Norfund (Norway’s development finance institution), and Ghana’s Ashesi University Foundation.

“We don’t take that for granted,” he said. 

More than 76% of consumers in India prefer talking to businesses over a phone call, according to a recent study from Truecaller. Since voice is still consumers’ preferred way to communicate, that leaves a big opportunity to automate support and outreach calls using voice AI in the country. Voice AI startup Ringg, which already processes 20 million call attempts a month, is betting that volume keeps climbing over the coming months, and it just raised more money on that belief.

The company said today it has landed $10 million from Peak XV Partners as an extension of its Series A. It had previously raised $5.5 million in a Series A round earlier this year, bringing the round’s total to $15.5 million.

Ringg started life as a text-to-speech startup called DesiVocal, but training its own speech models proved expensive, so the founders moved up the stack — building voice AI agents for enterprises instead. Indian fintech Cred became its first customer, and Ringg has since signed Indian startups like Flipkart, Practo, Groww, and Policybazaar.

“At the start, we were doing high-volume, low-complexity use cases like outbound calling, lead qualification, loan collection, and more. We quickly realized these are not sticky use cases, and so it’s always going to be a price game,” the startup’s co-founder Siddharth Tripathi told TechCrunch.

Ringg still serves some of those simpler use cases, but it has set its sights on more complex workflows: appointment booking for healthcare clinics, abandoned-cart recovery for e-commerce sites, and onboarding/KYC (“know your customer”) checks for fintech apps.

Tripathi said Ringg’s voice agent now runs across 1,200 clinics for the healthcare app Practo, helping patients book visits or follow up on next steps post-visit.

Voice calls still make up over 70% of Ringg’s business, but the startup has started branching into other channels, including chat and WhatsApp. For some clients like Shell, it’s also automating browser-based support requests.

“We are trying to position ourselves as a platform for agents that bring outcomes or get things done rather than voice agents for enterprises,” Tripathi said.

Most of Ringg’s customers are based in India, with a handful in the Middle East and the U.S. But the startup isn’t trying to sell directly to U.S. companies; instead, it wants to partner with global capability centers in India — the offshore hubs multinationals increasingly lean on for back-office and support work — to sell automation capacity alongside human support.

Tripathi said the company builds its own speech recognition and generation models and would eventually like to own the full voice stack, including infrastructure and deployment. For now, though, that remains too costly, so the product works as an orchestration layer, routing tasks to different models depending on the use case.

Rishen Kapoor, a principal at Peak XV, said that because Ringg started as a research lab building its own models, that technical depth shows up in the complex use cases it’s now tackling.

“Because of the technical capabilities, they can actually do these hard-won enterprise workflows end to end. They can complete these higher-value tasks like merchant onboarding, like L1 and L2 support, with quality and with consistency,” Kapoor told TechCrunch.

Voice AI in India is a crowded field. Model makers, including Deepgram, ElevenLabs, Cartesia, and local players like Sarvam and Smallest.ai, are all jockeying for pole position. Orchestration-focused startups like Bolna and Blue Machines are chasing the same layer Ringg occupies, while sector-focused players like Gnani and Arrowhead concentrate heavily on finance.

That layered stack — model makers, orchestrators, and application-layer players all trying to lock in enterprise workflows — is itself the story. The money and the defensibility increasingly sit with whoever owns the customer relationship and the outcome.

Ringg currently has 40 employees, with more than 15 hired in the last three months. The startup is hiring for forward-deployed engineer roles that combine technical chops with product management skills, along with researchers focused on bringing down the cost of running its models.

Generalist, a robotics startup, is now valued at $3 billion after raising additional capital led by 8VC, according to two people with knowledge of the funding.

The fresh capital totals nearly $200 million according to a regulatory filing. That additional capital is an extension of a $400 million Series B led by Radical Ventures that the company announced in June at a $2 billion valuation, the people said. The new capital brings the round’s total funding to $600 million.

Generalist and 8VC didn’t respond to a request for comment.

Generalist was founded in 2024 by former Google DeepMind researchers Pete Florence and Andy Zeng, along with former Boston Dynamics engineer Andrew Barry. It received early backing from 8VC and Radical Ventures as well as Nvidia, Union Square Ventures, Bezos Expeditions, and AI researcher Fei-Fei Li.

Until recently, the startup operated quietly and with little publicity.

Generalist is developing an AI foundation model that can work with various robots. It claims its newly released Gen 1.5 model enables robots to master new tasks from video demonstrations as short as 3 to 12 seconds long.

The startup is working with a handful of customers, using their feedback to tailor the model for specific use cases, according to one source.

Generalist isn’t alone in its pursuit of building a brain for a broad range of robots. Other competitors include Physical Intelligence, which is reportedly valued at $11 billion, and SoftBank-backed Skild AI, valued at $14 billion, as well as Genesis AI, which was in talks as of last month to raise capital at a $3 billion valuation.

The funding surge reflects a bet from some investors that robotics may soon reach its own “ChatGPT moment,” meaning that robots will be able to perform general tasks without being explicitly trained for each one. However, because robots cannot be trained on the entirety of the internet’s data the way LLMs can, some VCs warn that a truly general robotics model may still be years away.

Nitter, an open source project that allowed people to read X posts without logging into or even opening the X app, has received cease-and-desist letters from X demanding that it shut down. The news was shared via a brief message posted to the project’s website, and follows X’s earlier attempts to knock Nitter offline by technical means.

The service also powers a number of other sites, including XCancel, that allow people to view X posts directly.

This isn’t X’s first attempt to shut down Nitter. In 2024, Nitter’s flagship instance, Nitter.net, went dark temporarily after X rolled out new API restrictions. Nitter worked by fetching public X posts and then stripping out the ads, tracking cookies, and JavaScript, giving people a clean, clutter-free way to read posts without an account or the app.

After that crackdown, those who wanted to host a Nitter instance had to connect it to a real X account, according to the project’s GitHub page. Despite the restrictions, development picked back up and Nitter instances came back online.

This time, X is working to shut down Nitter and its instances via legal means. Nitter’s website states that the Nitter.net project is offline while its creator seeks legal advice after receiving a cease-and-desist letter. That creator, a developer who goes by the handle Zedeus, told TechCrunch by email that other Nitter instances received similar letters.

On Nitter’s website, the message currently reads:

On 24 August 2026 cease and desist letters have been sent by X Corp. demanding a permanent takedown of Nitter instances and the project’s repository.

nitter.net is offline and development has stopped for the time being. I’m seeking legal advice and won’t be commenting further on the specifics for now.

Thank you to everyone who used, hosted, packaged, donated and contributed to Nitter over the past seven years.

The letter from X, which TechCrunch has viewed, accuses Nitter of an “unlawful use and circumvention of X’s Application Programming Interface (API) and associated data,” through its service, saying that X has evidence that Nitter scraped X data and accessed X accounts and session tokens in violation of X’s rules.

Lawyers for X said the actions are in violation of “various state and federal laws, including, but not limited to, the Texas Harmful Access by Computer Act (§ 143.001 and § 33.02) and the Lanham Act (15 U.S.C. §§ 1114, 1125).” The letter gave Nitter until 5 p.m. EST on August 25 to shut down.

X is hardly alone in policing alleged scrapers. Meta has taken numerous scrapers to court, and most larger social networks today restrict the use of third-party readers, forcing users to log in and access the site’s content through the official app, where they can be tracked and shown personalized ads.

It’s an unfortunate development for lurkers, given that Nitter and its instances offered a handy way to keep up with certain people’s posts on X without an account. Now those people will either need to give up that access or, as X likely hopes, create an account and log in.