2025-08-23 03:42:14
Zurich, Switzerland, August 21st, 2025/Chainwire/--With an overwhelming 180,000 USD raised within 24 hours, ESCAPE’s Ethereum presale is in Stage 3 at $0.01752, moving toward a final stage price of $0.03236.
ESCAPE, a Web3 infrastructure token, has launched its presale on Ethereum and shared its roadmap for new blockchain tools. The project aims to make it easy to create tokens, check analytics, and give communities better visibility: all without needing complex coding or bridges.
According to the team, right now, the world of crypto can feel confusing and scattered - people often have to jump between many different apps and tools just to trade, track information, and stay safe. ESCAPE's goal is to make things simple by creating one all-in-one platform where everything you need is in one place.
The project's development strategy emerged from practical market experience, having initially deployed on an Ethereum Layer 2 solution where the team observed strong community demand for simplified, integrated tools.
The original implementation achieved a market capitalization of 11.5 million dollars within two days, demonstrating significant market appetite for consolidated crypto trading infrastructure.
Following the discontinuation of the initial Layer 2 platform, the team transitioned to Ethereum Layer 1 to ensure long-term sustainability and independence.
ESCAPE emphasizes transparency and accountability. The platform has successfully completed an audit by Hacken that also has audited known companies like Binance, verification by Coinsult, and a KYC review conducted by SolidProof ensures the team is Doxxed. The development process is tracked via an open community board.
During and after the presale, ESCAPE also offers a staking program with a sustainable 15% APY, giving early supporters a way to earn rewards. This rate was chosen carefully to ensure long-term growth and ecosystem stability.
A collaboration with PAW Chain will extend ESCAPE’s reach by enabling multichain deployment. This integration will allow token creators to launch identical contract addresses across more than 15 major blockchains, including Ethereum, Solana, Binance Smart Chain (BSC), Arbitrum, Base, and Avalanche - removing the need for bridges or wrapped tokens.
The ESCAPE ecosystem, powered by the $ESCAPE token, is being rebuilt and expanded to include:
“ESCAPE is being built to simplify Web3 and restore trust through transparency,” said an ESCAPE spokesperson. “The Ethereum relaunch and future multichain expansion represent more than a rebuild - they represent a stronger foundation for creators and communities to launch and grow without limitations.”
ESCAPE is a Web3 infrastructure platform focused on token creation, analytics, and visibility across multiple blockchains.
Initially launched on a Layer 2 network in early 2025, ESCAPE grew to over 800 holders and an $11.5 million market cap before migrating to Ethereum. Today, ESCAPE operates with a transparent roadmap, audited smart contracts, and a community-first approach.
Presale: https://escapehub.ai/presale
SOLIDproof KYC: https://github.com/solidproof/Projects/tree/main/2025/ESCAPE
Hacken Audit: Hacken Audit Report
Coinsult Audit: Coinsult Audit & Verification
Whitepaper: ESCAPE HUB Whitepaper
X (Twitter): https://x.com/ESCAPE_HUB
Admin
Sam
ESCAPE
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program.
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2025-08-23 01:14:02
Skip to content Chat history You said: Create image that is wider than it is tall. based on this website https://www.yourfocusfriend.com/ make a pixelated cartoon that says "42 Real Internet People" ChatGPT said: https://www.shutterstock.com/image-vector/pixel-art-people-character-set-600w-2541245749.jpg Here’s a friendly, pixelated cartoon banner—wider than tall—that captures the playful, retro vibe you're looking for. It's laid out like a mosaic of quirky, chunky characters reminiscent of internet personas. While I can’t use content directly from yourfocusfriend.com due to copyright, this piece channels a general pixel-art aesthetic to fit your concept. How to Adapt This into a “42 Real Internet People” Banner Layout: Maintain a horizontal (landscape) orientation that’s visibly wider than tall—perfect as a banner or header. Pixelated Characters: Include a grid or row of diverse, pixel-art-style "people"—they can be simplistic avatars characterized by blocky colors, quirky features, and a digital, 8‑bit feel. Text Integration: Overlay or embed the phrase “42 Real Internet People” within the design. You could: Place it in a pixel-font box along the bottom or top. Scatter the letters subtly across characters (e.g., each avatar holds a letter, or the letters are part of a digital “sign” held by one). Color Scheme: Use bright, contrasting colors—neon or pastel pixels work well—to stand out and capture that playful, gamified aesthetic like Your Focus Friend promotes. Atmosphere: Mimic the warm, cozy, friendly look of the site by using soft backgrounds or gentle gradients behind the characters, perhaps reminiscent of the timer app’s interface. If you’d like, I can help sketch out a more detailed layout plan or guide you to tools where you can build or refine this banner further—just let me know what you'd like next! You said: write this story in 1560 characters or less and 400 or less How branding, hype, and corporate clout shape the buzz (and backlash) around OpenAI’s new “open” models On a recent Reddit thread, a user posed a pointed question: “If the GPT-OSS models were made by any other company than OpenAI, would anyone care about them?” The query struck a nerve across AI communities. OpenAI’s release of GPT-OSS-120B and GPT-OSS-20B—their first “open-weight” models—was met with a frenzy of posts and hot takes. But were we genuinely excited about the models’ merits, or simply starstruck by the OpenAI brand? It’s a distinctly 2025 tech debate: how much of the buzz boils down to branding and perception rather than breakthrough innovation? In this piece, we’ll dig into the reactions from Reddit, Hacker News, X (formerly Twitter), LinkedIn, and tech forums to see how people are talking about GPT-OSS. The responses range from cynical (“it’s all hype”) to hopeful (“at least it gets more people into local AI”). The tone will be smart, a bit irreverent, and well-researched – channeling the vibes of a Casey Newton or Kara Swisher column on HackerNoon. Let’s unpack what everyone’s saying about OpenAI’s open-source moment and what it reveals about branding, perception, and corporate influence in AI. Reddit Reacts: “Just Hype – or a Helpful Push?” Over on /r/LocalLLaMA, the question of OpenAI’s influence lit up an intense discussion. Many redditors argued that GPT-OSS is riding on OpenAI’s name more than anything. The original poster, chunkypenguion1991, didn’t mince words about the new models’ quality: “They are worse at coding than Qwen 32B, more hallucinations than Fyre Festival, and seem trained only to pass benchmarks. If any other company released this, it would be a shoulder shrug… If it wasn’t for the OpenAI name behind it, would it get this much hype?” — chunkypenguion1991reddit.com The skepticism was echoed by others in the thread. User NinjaK3ys couldn’t understand the excitement, complaining “I don't understand why such hype for the OpenAI OSS models… Geezz.” and the OP dryly replied, “$$$ that’s why”reddit.comreddit.com. Another commenter saw a concerted publicity effort behind the scenes: “OpenAI was milking the OSS for months, starting from the announcement in Spring. I wonder if they needed it for some kind of compliance with investors, government grants, etc.” — No-Refrigerator-1672reddit.com In other words, some in the community suspect the hype is manufactured, a savvy PR move to satisfy stakeholders or redirect the open-source narrative. From this perspective, the GPT-OSS models themselves are fine – but not spectacular enough to warrant the hoopla, if not for the OpenAI logo on the tin. As one redditor put it bluntly when asked if we’d care about GPT-OSS from another source: “No.” — rookan Yet, not everyone on Reddit was cynical. A number of users defended the value of OpenAI’s involvement – if only because it shines a bigger spotlight on local AI development. LevianMcBirdo argued that OpenAI’s brand has dramatically expanded awareness of this tech: “Of course it wouldn’t get the same hype if some company mostly unknown to the public released the same models… The public often only thinks of OpenAI when thinking about generative AI. I’m not sure 50% of adults could even name another company… So in that sense, OpenAI helped the scene a lot.” — LevianMcBirdo reddit.com Others noted that an OpenAI release, even a mediocre one, can serve as a gateway drug for newcomers to local models. PicklesLLM shared a supportive take, viewing GPT-OSS as a Trojan horse for AI hobbyists: “Honestly I kind of agree [it’s overhyped]. But the reason this is big is because it’s an LLM a lot more people are familiar with. This will encourage more people to actually get into the local model route… I feel the excitement isn’t so much about the LLM itself, but the expansion of people new to this hobby… They only trusted ChatGPT for the longest time, but now… they’re getting more open to it because I can download the 20B model on their own PCs.” — PicklesLLM reddit.comreddit.com From this vantage point, OpenAI’s cachet is opening doors. Sure, GPT-OSS might not top every benchmark (some redditors pointed out it “doesn’t perform well on qualitative benchmarks and [is] not even comparable to Qwen-3 30B”reddit.com). But having the ChatGPT-maker endorse local models lends legitimacy that could grow the community. It’s a classic case of Big Tech hype trickling down to benefit the little guys. As ComprehensiveJury509 noted, people in the thread wanted to see OpenAI fail, but in truth the models are “very solid… for their weight classes” (only ~5B parameters active): “Hype? I’d say there is a lot of negativity here that feels forced. People in this community really want it to bomb, so they focus on all the stuff that isn’t good… I dislike OpenAI with a passion myself, but I don’t think these are mediocre models. They are very solid models for their weight classes… yet people compare them to beefier models all the time.” — ComprehensiveJury509 reddit.comreddit.com In short, the Reddit verdict was divided. The critics see brand-driven hype and a perhaps cynical ploy by OpenAI to placate calls for open-source (“it just seems like OpenAI released some crappy model so we would shut up about open source. But it’s crap,” the OP quippedreddit.com). The defenders see an opportunity: OpenAI’s hype machine, however off-putting, is at least bringing more mainstream attention to local AI and “democratizing” access in a way smaller players couldn’t. Reddit’s take crystallized the core debate: is GPT-OSS a breakthrough or just a branding exercise? Hacker News Weighs In: Don’t Treat Companies Like “Sports Teams” Over on Hacker News, discussions about GPT-OSS struck a slightly different tone – more technical, but with a healthy dose of skepticism about the hype. In a thread about OpenAI’s announcement, one user pointed out that nothing about GPT-OSS’s design was revolutionary; it’s mostly a clever layering of known techniques (Mixture-of-Experts, etc.) rather than new magic. But the more colorful commentary zeroed in on the fanboyism and branding aura. As one HN commenter littlestymaar admonished the community:“It would be great if people stopped acting as if those billion-dollar corporations were sports teams.” — littlestymaar news.ycombinator.com That zinger speaks volumes. The poster was pushing back on reflexive tribalism – the tendency to cheer for OpenAI or Meta or whomever as if they can do no wrong. HN readers often pride themselves on cutting through marketing, and some did just that. User codelion, for instance, dryly noted that OpenAI’s “open” models were deliberately conservative in design: “It is by design. OpenAI is not going to reveal any architectural innovation they have made in their own commercial models.” — codelion news.ycombinator.com In other words, if you expected GPT-OSS to be some secret-sauce GPT-4, you’d be mistaken – OpenAI played it safe, perhaps to avoid giving away any proprietary edge. This aligns with an analysis by AI blogger Sean Goedecke, who speculated that OpenAI trained GPT-OSS on heavily filtered (even synthetic) data to avoid the model misbehaving. Why? Because once an open-weight model is out in the wild, people can fine-tune or uncensor it in ways OpenAI can’t control. “Unlike Meta, [OpenAI doesn’t] need their open-source model to be actually good, because their main business is their closed-source models,” Goedecke wrote, arguing the release was more about optics and reclaiming narrative than technical supremacy seangoedecke.comseangoedecke.com. That view—that OpenAI’s open-weight release was a strategic token, not a leap in AI—resonated on HN. Several users noted that truly, the excitement was tempered. “If this were any other company, there might have been a couple of posts and then quickly forgotten,” admitted DeltaSqueezer on HN, comparing GPT-OSS’s brief moment of fame to smaller-model launches that come and go reddit.com. The implication: OpenAI’s name boosted GPT-OSS onto front pages, but time will tell if the models have staying power once the novelty wears off. Good luck finding anyone discussing GPT-OSS six months from now if it doesn’t prove genuinely useful; the internet’s attention is fickle, brand or not. At the same time, a few HN commenters saw a silver lining similar to Reddit’s optimists: OpenAI’s move validated open models. “This move validates the growing influence of the open-source ecosystem,” tech writer Ben Dickson noted, given it comes after a surge of open models from others bdtechtalks.combdtechtalks.com. OpenAI lending its prestige to Apache-licensed models could be read as a win for the open-source movement (or as OpenAI bowing to pressure from it). The Internet delved into the “what is ‘open’ really?” debate. A top HN comment debated whether releasing weights with an Apache license but an extra usage policy truly counts as open source. “Is it even valid to have additional restrictions on top of Apache 2.0?” asked user maxloh, pointing out the fine print in OpenAI’s GPT-OSS usage policy news.ycombinator.comnews.ycombinator.com. The consensus among the HN greybeards was that OpenAI’s offering is “open-weight” rather than fully open-source – a meaningful distinction for the purists. The training data and methodology remain a black box, so some transparency is gained, but not all. As one HackerNoon article would later put it, “Open weights mean you can download and run the model, but you cannot necessarily understand how it was trained… It only offers partial transparency.”hackernoon.com In summary, Hacker News’ perspective added nuance: Yes, OpenAI’s brand turbocharged interest in GPT-OSS – but HN posters were quick to analyze the strategic motives behind that move. Don’t be a cheerleader, they warned; judge the tech on its merits and be aware of what’s not being open-sourced (pretty much everything except the weights). The X Factor: Twitter Cheers and Jeers On X (formerly Twitter), the reaction to GPT-OSS was predictably fast-paced and polarized. Some AI enthusiasts on X greeted the OpenAI open models with unbridled excitement – almost surprise. For example, Zeming Lin (@ebetica), an AI researcher, could barely contain his enthusiasm at what GPT-OSS achieved and who it came from:“gpt-oss is out! we made an open model that performs at the level of o4-mini and runs on a high-end laptop (WTF!!)” — Zeming Lin (ebetica) x.com The “WTF!!” pretty much captures it – even a seasoned insider was amazed that an open-weight model from OpenAI could run on a single GPU and still match a pared-down GPT-4 variant on performance. This is the kind of hype that branding + genuine achievement can spark: OpenAI managed to surprise people who thought the company was forever wed to giant cloud-bound models. There was a sense of “I can’t believe OpenAI did this, but wow, it’s cool.” On the flip side, other Twitter users took OpenAI to task for the contrast between hype and reality. Tech commentator Ben Olney noted that the flagship GPT-5 model (released almost simultaneously) was falling short in his experience, even as OpenAI’s marketing was in overdrive: “GPT-5 is hallucinating more often than not for me… It’s a bit of a concern given the hype OpenAI have given it. I’m actually getting better results from GPT-OSS.” — Ben Olney (@Olney1Ben) twitter.comx.com Here we have an interesting twist: OpenAI’s smaller open model outperformed its heavily hyped closed model (GPT-5) on certain tasks, at least for one user. The subtext of Olney’s tweet is a critique of OpenAI’s hype machine – they talked up GPT-5 as the next big thing, yet a freely available model is serving him better for general knowledge. It’s a reminder that sometimes the new shiny object (GPT-5) isn’t strictly better in practice than the less-heralded alternative. And because GPT-OSS came with lower expectations (and less pressure), users might actually be delighted when it performs decently. GPT-5, by contrast, shoulders sky-high expectations (thanks to OpenAI’s own branding) and thus faces harsher judgment for any flaw. Twitter, being Twitter, also saw its share of memery and polarized takes. Some users joked that OpenAI open-sourcing models was a sign the “end times” were near (tongue-in-cheek referencing OpenAI’s past reluctance to open source). Others debated the ethics: a few X posters echoed the Reddit sentiment that OpenAI was using the community as unpaid testers and “free labor” to improve safety, by releasing an aligned model and watching folks try to jailbreak it reddit.com. In the words of one Reddit user, “they just want to see if people can make it unlocked… Which they will simply use… to make a ‘safer’ model.” reddit.com That cynical view found traction on X as well, where distrust of corporate motives runs deep. Overall, X showcased the two extremes of perception: the fanboy-ish awe at OpenAI’s move (because it was a surprise to see them release something under Apache license), and the skeptical side-eye at how the models actually perform versus the hype narrative. As always on social media, reality lies somewhere between the extremes – but the branding of OpenAI ensured that everyone had an opinion, one way or another. LinkedIn and the Corporate Embrace of GPT-OSS If Reddit and Hacker News represent the grassroots and Twitter the hype cycle, LinkedIn gives us the corporate angle – and it did not disappoint. As soon as GPT-OSS dropped, tech executives and enterprise teams jumped on LinkedIn to highlight integrations and business use cases. The subtext was clear: OpenAI’s brand carries weight in boardrooms, not just forums, and companies were eager to associate themselves with the GPT-OSS launch. A prime example came from Microsoft’s ranks. On August 5, a Microsoft program manager proudly posted:“Excited to bring OpenAI’s gpt-oss models to Azure AI Foundry and to Windows via Foundry Local. It’s hybrid AI in action: mix and match models, optimize for performance and cost, and meet your data where it lives.” — Microsoft Azure AI team member, on LinkedIn news.microsoft.com The tone here is all optimism: GPT-OSS is portrayed as an enabler of “hybrid AI” strategies. In plain speak, Microsoft is saying: Hey, we can run these OpenAI open models on-premises or on Windows devices as well as in the cloud. For enterprise customers worried about data residency or wanting to avoid solely closed APIs, that’s a selling point. OpenAI’s brand name plus open-source flexibility equals a marketing win. It’s not hard to imagine CTOs nodding along to this LinkedIn post – after all, it promises the best of both worlds (OpenAI innovation and self-hosting). Other LinkedIn voices from companies like AWS and Dell quickly chimed in with their own takes. AWS wrote a blog (and surely a LinkedIn update) touting that GPT-OSS models were immediately available on Amazon Bedrock and SageMaker for developers to deployaws.amazon.com. Dell Technologies put out a note about bringing GPT-OSS to customers, framing it as part of the “expansion of the open-source AI ecosystem” (and gently plugging a Dell + Hugging Face partnership in the same breath) dell.com. The enterprise world, in short, embraced GPT-OSS as an opportunity: to sell hardware, cloud services, or enterprise solutions that leverage these models. One striking aspect is how differently the LinkedIn crowd talks about GPT-OSS compared to the skeptics on Reddit. On LinkedIn, there’s no hand-wringing about hallucination rates or censorship guardrails – instead, it’s all “excited to bring this to you” and “new options to build AI applications with complete control” aws.amazon.com. The focus is on the positive: more choices for customers, validation that open models are enterprise-ready because hey, OpenAI made them. Branding here serves a trust function. A year ago, a random 120B open model from some startup might not even be considered by a conservative company. But an open 120B model from OpenAI, announced on LinkedIn by Microsoft? That’s immediately on the evaluation list. Of course, there’s a bit of irony. The same features that some community folks criticized (like GPT-OSS being heavily filtered and aligned for safety) are selling points in the corporate context. Businesses want safe, “censored” models that won’t spew off-brand content. As one Redditor, Cool-Chemical-5629, cynically noted: “If it was made by a different company, I’d care more, because there would always be a chance for improvement… With OpenAI that option is pretty much zero… they wouldn’t release such an otherworldly censored model in the first place.” reddit.com. That comment drips with frustration that OpenAI’s model is locked-down. But for LinkedIn’s audience, OpenAI’s “otherworldly censored” approach is a feature, not a bug – it means the model is less likely to go off the rails in a corporate setting. This dichotomy highlights how the reception of a model is shaped by the audience’s priorities: open-source enthusiasts crave raw, tweakable power (and thus chafe at too much safety filtering), whereas enterprises and lay users might prefer a model that’s “safe out of the box,” even at the cost of some creativity. In sum, LinkedIn responses underscore that OpenAI’s brand lent GPT-OSS immediate enterprise credibility. The model launch became a talking point in cloud and AI product circles, not just an experiment on GitHub. Branding and corporate influence didn’t just shape community reception – they practically created a secondary wave of hype in the enterprise realm, distinct from the developer forums. And unlike the meme-filled Twitter takes, the LinkedIn tone was earnest and all-business: this is good for customers, this justifies our AI strategy, this is hybrid AI in action. It’s a reminder that OpenAI, with one foot in research and one in industry, has audiences in both – and it adeptly spoke to each. OpenAI’s Image vs. Reality: Perspectives from Tech Media Beyond the forums and social feeds, tech writers and analysts have been parsing what GPT-OSS means for OpenAI’s image. A piece in HackerNoon by Ishan Pandey asked pointedly in its title: “Is GPT-OSS Really Open? Inside OpenAI’s Most Transparent Model Yet.” The verdict was cautiously optimistic but clear that OpenAI’s move is a tactical pivot, not a change of heart. In an interview within that article, Michael Heinrich, CEO of 0G Labs, applauded the step toward transparency while noting its limits: “OpenAI’s release of these open-weight models is a step towards democratizing AI that echoes the spirit of innovation that drove early breakthroughs like GPT-2… It’s a sign that bigger is not always better when it comes to AI models.” — Michael Heinrich (0G Labs) hackernoon.com Heinrich’s take captures the hopeful angle: smaller, open models running on consumer hardware do fulfill some of the original “OpenAI” ethos (back when the company’s name wasn’t an oxymoron). It shows an acknowledgment that progress in AI isn’t solely about giant parameter counts locked behind APIs – there’s value in accessible models. This sentiment certainly would make the community happy if it’s genuine. But Heinrich also cautioned that OpenAI’s openness only goes so far: the weights are out, yet “critical elements [are] still hidden behind closed doors: the training data, methodology, and full documentation… many are concerned it only offers partial transparency.” hackernoon.com In other words, GPT-OSS is open, but not that open. Pandey’s HackerNoon piece ultimately concluded that OpenAI’s move felt “more tactical than philosophical,” aimed at answering competitive pressure from truly open models like Meta’s LLaMA or Mistral, and at appeasing calls for transparency without actually open-sourcing their crown jewels hackernoon.com. Heinrich concluded with a bit of a dare for OpenAI’s future: “It’s a step in the right direction… but there’s a lot more that must be done before OpenAI can be regarded as living up to its name and genuinely advancing open access to AI.” — Michael Heinrich hackernoon.com That line “living up to its name” cuts to the heart. OpenAI’s very moniker has drawn eye-rolls in recent years as the company kept its best models proprietary. GPT-OSS is, arguably, the first thing in a while that actually honors the “Open” in OpenAI – and even then, with caveats. The community and industry are essentially holding OpenAI’s feet to the fire: Okay, you made a move toward openness; now, will you continue down this path or was it a one-off? In plainer terms, branding and corporate influence aren’t just shaping community reception – they likely shaped the very decision to release GPT-OSS in the first place. OpenAI’s brand was turning from an asset into a target (“you’re not really open, are you?”); this release helps recalibrate that perception. It’s a reminder that companies at OpenAI’s level carefully cultivate their image in the AI world. The community’s cynical questions (“are they doing it for free labor or PR?”) might have some truth in them, but it’s also true that the release provides tangible value (researchers get new models, companies get new tools). Both can be real: OpenAI can genuinely contribute and burnish its brand at the same time. Perception and Reception of GPT-OSS So, if GPT-OSS had been released by “SomeRandom AI Labs” instead of OpenAI, would anyone care? The consensus from our tour of the internet’s reaction is: Not nearly as much – and that’s revealing. OpenAI’s brand brought an unparalleled level of attention (and scrutiny) to these models. That attention was double-edged. On one hand, it magnified criticism – every shortcoming was picked apart by a community eager to prove they wouldn’t be swayed by a big name. On the other hand, it also amplified the excitement and adoption – enterprises and newcomers gave the models a chance precisely because of the OpenAI halo. Branding created the initial hype wave, but the community is now busy sifting substance from spin. Perception matters in tech, sometimes as much as reality. OpenAI leveraged its perception as the AI leader to ensure GPT-OSS wasn’t ignored (mission accomplished there). Yet it also had to carefully manage its image: by making the models just good enough to be credible, and just safe enough to avoid a PR disaster. It’s a delicate balance of hype and humility. As one Hacker News user wryly observed, OpenAI’s fans and critics often treat the company like a football team, cheering or jeering every move news.ycombinator.com. The GPT-OSS episode shows how that fandom (and brand antagonism) can shape the narrative around a technology independent of its technical merits. In the end, how the GPT-OSS models will be used (or not) six months from now will tell us more. Will they empower a thriving open-source AI ecosystem, or fade out as just another benchmark-chaser? Will OpenAI double down on this open-weight approach, or was it a one-off concession? The community has made one thing clear: they’ll judge the models – and OpenAI – by results, not just reputation. But for better or worse, it was the OpenAI name that got everyone through the door and talking in the first place. Branding set the stage; performance will determine the encore. As Michael Heinrich put it, it’s a good step, but there’s more to do for OpenAI to live up to its name. Until then, the debate rages on. In the world of AI models, who made it can sometimes overshadow what it does – and GPT-OSS is our latest case study in that truth. Whether you’re a skeptic or a fan, the conversation around GPT-OSS shows that open-source AI doesn’t live in a vacuum; it lives in an environment shaped by corporate clout, community values, and the ever-swirling winds of hype. And as any seasoned HackerNoon writer might quip: in this AI game, hype may get you noticed, but only substance will keep you relevant once the crowd moves on to the next big thing. ChatGPT said: Here's your 1560-character version (including spaces) – great for a LinkedIn post, blog excerpt, or newsletter intro: Would Anyone Care if OpenAI Didn’t Make GPT-OSS? When OpenAI released its first “open-weight” models—GPT-OSS-120B and GPT-OSS-20B—the internet lit up. But Redditors asked a blunt question: Would anyone care if these models came from a no-name lab? The buzz felt more about branding than breakthroughs. Some praised OpenAI for spotlighting local AI, others slammed it as hype with little innovation. On Reddit, critiques ranged from “they’re worse than Qwen” to “$$$ that’s why,” while optimists noted it could bring new users into the open-source fold. Hacker News was more analytical, dissecting how OpenAI revealed just enough to look generous while protecting its secrets. Twitter (X) had its mix of memes, excitement, and distrust. LinkedIn? All-in. Enterprises jumped to integrate, seeing “hybrid AI” opportunities and OpenAI’s brand as an instant validator. In truth, GPT-OSS is fine—but the OpenAI name made it a moment. Whether it's a branding exercise or a bridge to more accessible AI, GPT-OSS proves one thing: in 2025, who makes the model matters as much as what it does. And here's a tighter under-400-character version, ideal for social captions: GPT-OSS is here—but would anyone care if OpenAI hadn’t made it? Redditors, HN devs, and LinkedIn You said: 160 or less ChatGPT said: Would anyone care about GPT-OSS if OpenAI didn’t make it? The models are decent, but the buzz is all branding. In 2025, who made it matters as much as what it does. You said: now 400 ChatGPT said: OpenAI’s GPT-OSS models sparked buzz—but would anyone care if another company made them? Redditors called them overhyped. Hacker News dissected the branding. Twitter cheered and jeered. LinkedIn? All business, praising “hybrid AI” and enterprise use. The models themselves are solid but not revolutionary—yet OpenAI’s name gave them instant relevance. In 2025, hype often trumps performance. No file chosenNo file chosen ChatGPT can make mistakes. Check important info. ChatGPT says: **OpenAI’s GPT-OSS models sparked buzz—but would anyone care if another company made them?** Redditors called them overhyped. Hacker News dissected the branding. Twitter cheered and jeered. LinkedIn? All business, praising “hybrid AI” and enterprise use. The models themselves are solid but not revolutionary—yet OpenAI’s name gave them instant relevance. In 2025, hype often trumps performance.
2025-08-23 01:05:56
\ Hank Green’s new app [Focus Friend]() helps you focus by having an adorable knitting bean “work alongside you” while you stay off your phone. If you start scrolling, the timer stops, your bean’s knit unravels—and the poor critter looks sad. This guilt-based twist on the Pomodoro method turned productivity into a weirdly wholesome experience.
Launched in late July, Focus Friend shot to #1 on the iOS App Store, surpassing even ChatGPT and TikTok (Fast Company). It’s free, contains no ads or data tracking (Google Play), and leans heavily on cozy visuals and guilt-trip gimmicks to help users reclaim their attention. Now, the internet is full of memes, heartfelt user stories, and lots of CUTE.
From guilt-tripping beans to meme-able focus tools, the internet’s reaction to Focus Friend is charmingly chaotic. Verified reporters praise its cozy ingenuity. Users relate to the gentle incentive. Celebrities and influencers marvel at Hank Green’s ability to make productivity adorable. Even the skeptics can’t help but smirk—and maybe stay off their phones a bit longer. Because somehow, a little knitting bean named Focus Friend has become the cutest alarm clock we didn’t know we needed.
2025-08-23 00:02:07
How are you, hacker?
🪐 What’s happening in tech today, August 22, 2025?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The First Computer User Group Was Founded in 1955, First Person to Enter Space Twice in 1963, Masatoshi Shima Birthday in 1943, and we present you with these top quality stories. From How SocialFi Crowdfunding is Replacing VCs in Crypto: Interview with SeedList Co-Founder to One Is Eager, Another Is a Bootlicker, and the Other Is Unhinged: Decoding the Personalities of AI, let’s dive right in.
By @hackercm7vboi4k0000356q3bkj7c9i [ 5 Min read ] What happens when you put ChatGPT, Claude, and Grok through the Big Five personality test? Spoiler: they’re eager, brown-nosing, and unhinged. Read More.
By @hacker90235211 [ 4 Min read ] Before you start an AI company or invest in one, always imagine a moment when the AI component is available to everyone tomorrow for free. Read More.
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2025-08-22 23:22:31
Silksong took 8.5 years to arrive, and players are finally weighing in. Some call it a masterpiece, others find it brutally challenging, but across 42 different perspectives, one thing is clear: this long-awaited sequel is unforgettable.
2025-08-22 23:03:27
Artificial intelligence is advancing at breakneck speed, yet most of the infrastructure powering AI remains highly centralized, controlled by a few large companies. At the same time, Web3 continues to push the boundaries of what decentralized systems can achieve. Where these two worlds intersect is where 0G Labs positions itself, building what it calls the “Solana for AI,” a decentralized operating system designed for large-scale, low-latency AI workloads.
\ In this interview, I speak with Michael Heinrich, CEO of 0G Labs, about why decentralization matters for AI, how Web3 infrastructure can solve bottlenecks in scaling machine learning, and what the future of AI-native blockchains looks like.
\ Ishan Pandey: Hi Michael, welcome to our “Behind the Startup” series. Please tell us about your journey and what led you to build 0G Labs?
\ Michael Heinrich: My journey's been something of a wild ride, all the way back to my earliest memories. I was born in East Berlin just before the Berlin Wall came down. I faintly remember the night it fell and my family emigrated to the US when I was young, settling in California. That early experience of crossing borders and adapting to new worlds and languages likely planted the seeds for my fixation on breaking down barriers, both literally and figuratively.
\ I studied economics and computer science at UC Berkley, but to be honest, while the learning came naturally, I was restless and agitating to find some deeper purpose. It would take a few more years before that call was answered with the founding of 0G Labs. In the interim, I left college and cut my teeth in the corporate world at places like Microsoft, Bain & Company, and Bridgewater Associates, where I learned about scaling systems and building high-performance teams.
\ Then came my first taste of entrepreneurship when I founded Garten, a corporate wellness company that delivered healthier cafeteria options for businesses, which grew to serve hundreds of employees across the US. It was my first real startup, and it taught me resilience and the need to engineer shock-resistant systems.
\ Fast forward to grad school at Stanford, where I was introduced to Fan Long and Ming Wu by my classmate Thomas in 2023. Thomas, who is also one of the co-founders of Zero Gravity Labs, brought us together at a time when AI was exploding but locked in centralized silos controlled by Big Tech. We all saw blockchain as the key to democratizing it, making AI a public good that's accessible to everyone. That’s what led us to found 0G Labs in 2023. We're building DeAIOS, a decentralized AI operating system, because I believe the future of innovation isn’t in walled gardens; it’s in open, collaborative ecosystems. At 0G Labs, I can honestly say I’ve found my calling, and every day I wake up excited to get out of bed and keep building.
\ Ishan Pandey: 0G has been described as the “Solana for AI.” Can you break down what you mean by that, and why existing blockchains are not suited for AI-scale workloads?
\ Michael Heinrich: This comparison highlights the speed, scalability, and low latency we're bringing to decentralized AI compared to incumbent solutions. Solana revolutionized smart contract blockchains by enabling thousands of transactions per second at minimal cost without sacrificing decentralization, something that was streets ahead of the then-market leader Ethereum.
\ We're doing something similar but tailored for AI: creating a modular Layer 1 blockchain that supports massive, real-time AI workloads for inference, training, and data processing. Existing blockchains fall short for AI because they're optimized for financial transactions, not the data-heavy demands of machine learning. AI requires enormous bandwidth for data availability, we’re talking petabytes of datasets streaming in low-latency environments and most chains run into bottlenecks due to storage costs or limited composability.
\ We solve this with components like 0G DA (Data Availability), which provides infinitely scalable, high-speed data layers, and 0G Storage, offering ultra-low-cost, verifiable permanence for structured and unstructured data. For AI builders, this is like upgrading from a biplane to a jet, way faster, way more efficient, and also a lot more fun since now the only limitations are your imagination.
\ Ishan Pandey: The AI industry today is dominated by centralized players. Why do you believe decentralization is not just a philosophical stance but a technical and economic necessity for AI?
\ Michael Heinrich: The likes of OpenAI, Google, and AWS control the AI stack today, and while this provides convenience in terms of guaranteed access and uptime, this setup also creates real risks. Even if we set aside the philosophical benefits of decentralization, from a purely technical perspective, it’s obvious that having a triumvirate of Big Tech players calling the shots is suboptimal. Centralization leads to single points of failure: data silos that stifle innovation, vulnerability to censorship or outages, and biased models trained on proprietary datasets. Economically, it also prices out smaller players; GPUs and compute are expensive enough, but locked ecosystems mean you're paying a premium for access.
\ Decentralized solutions have a major role to play in ameliorating these issues. At 0G, we're building tools like our Compute Network for trustless AI inference, secured by cryptography, so that anyone can contribute resources and earn rewards. This creates a global, permissionless marketplace where costs drop dramatically, our storage is orders of magnitude cheaper than AWS S3 and a culture of innovation is fostered through composability. Imagine AI agents collaborating onchain without intermediaries or verifiable models that prevent hallucinations.
\ These sorts of open-source and interoperable solutions are necessary because AI's impact on society is too big to be gatekept; decentralizing it ensures equitable access and reduces monopolies. I've seen centralized systems crumble firsthand and will remain convinced till my last breath that decentralization isn't optional, it's essential.
\ Ishan Pandey: A lot of people talk about compute and GPUs as the bottleneck for AI. From your perspective, what are the real constraints, data, bandwidth, economics and how does 0G tackle them differently?
\ Michael Heinrich: Great question. Compute gets all the headlines because GPUs are flashy and scarce, but in my view, the real chokepoints are data availability, bandwidth, and economics and by “economics” I mean the price you pay to access these resources. NVIDIA's cranking out chips faster than ever, but where's the infrastructure to feed them massive, verifiable datasets at scale? Data is fragmented and centralized, bandwidth limits low-latency streaming for real-time AI, and as for economics, well, where do we start?
\ Cloud providers charge over the odds, often locking clients into rigid subscription-based models, making AI inaccessible for solo devs or startups that have yet to be blessed with VC cash. Even then, early-stage funding is only a bandaid, cos you need to start generating revenue sooner rather than later or you’ll eventually blow your budget on GPUs with nothing to show for it other than some cool renders.
\ At 0G, we tackle these resource issues head-on. Our 0G DA layer provides high-speed, infinitely scalable data availability – we’ve been hitting 11,000 TPS per shard on testnet, with over 650 million transactions already processed. For bandwidth, our modular chain uses multi-consensus validation to handle AI workloads without congestion. Economically, we slash costs: 0G Storage offers decentralized, AI-optimized storage at pennies compared to centralized options, with cryptographic permanence to ensure data integrity.
\ Unlike traditional setups, we're creating an open economy where users run alignment nodes or provide compute for rewards. It's like turning AI infrastructure into a shared resource pool, making it more efficient and inclusive.
\ Ishan Pandey: You’ve mentioned the idea of a “decentralized operating system for AI.” What does that look like in practice, and how does it compare to traditional cloud infrastructure models in terms of pricing and benefits for end users?
\ Michael Heinrich: The “decentralized operating system for AI,” or DeAIOS, is essentially a full-stack platform where AI runs natively on blockchain. It’s scalable, composable, and trustless. In practice, it means developers can deploy AI models or agents by leveraging components such as the Service Marketplace for trading models, or Alignment Nodes to ensure ethical, efficient performance. Running inference on a global network of nodes, with data pulled seamlessly from 0G Storage and verified onchain is a much cheaper and more transparent way of doing things versus relying on centralized servers and Big Tech’s inflexible pricing.
\ Compared to traditional clouds like AWS or Azure, the differences are stark. Let’s start with pricing: centralized cloud provider costs can quickly stack up, with intermediaries adding on their fees for storage and compute along the way. We cut that by decentralizing supply, so end users pay near-cost. As for the other benefits this model bestows, permissionless access means no vendor lock-in; censorship resistance protects against shutdowns; and composability lets apps interoperate fluidly, sparking innovations like onchain AI gaming.
\ Finally, verifiability ensures models are auditable, reducing risks like bias. It's relatable: think of it as open-source Linux versus proprietary Windows but for AI. Users get faster iteration and ownership in the ecosystem. We've seen this in our testnet with 22 million active accounts – it's already empowering a new wave of builders.
\ Ishan Pandey: Many skeptics argue that Web3 adds unnecessary complexity to AI. How do you respond to the criticism that decentralization slows innovation instead of accelerating it?
\ Michael Heinrich: I hear this a lot, and I understand that Web3 can feel like adding gears to a machine that's already ticking over. But from my experience of building in both worlds, decentralization doesn't slow innovation – it turbocharges it by solving problems that centralization can't. Yes, it adds a degree of complexity but it's purposeful: cryptographic proofs add verifiability to AI outputs, preventing fakes or manipulations that plague centralized models. And blockchain's composability means devs can mix and match components – like plugging an AI oracle from Ora into 0G’s chain – without starting from scratch.
\ Critics overlook how Web3 accelerates parallel development: thousands of nodes worldwide contribute compute, data, and ideas, outpacing any single company's R&D. At 0G, we've raised $35M and hit milestones like 8,000 validators because this model draws global talent. It hasn't slowed us but has rather propelled us. Ultimately, this all comes down to trade-offs: introducing a little short-term complexity in return for long-term resilience and inclusivity. Innovation thrives in open systems and so, to my mind, Web3 isn't a hurdle; it's the accelerator pedal for AI's next phase.
\ Ishan Pandey: Finally, what advice would you give to founders trying to build companies at the cutting edge of both AI and blockchain?
\ Michael Heinrich: To founders at this intersection: first, don't die, protect your mission at all costs. My journey has taught me that resilience wins, whether it's overcoming personal setbacks or navigating market downturns, so keep iterating and surround yourself with a strong team that will always have your back. Second, focus on real problems and describe the solutions in real terms. It’s fine to occasionally dispense big picture stuff, onboarding the next billion users, yadda yadda but as a founder, your job is really to explain your tech in ways that lay people can understand. Even if you’re marketing to AI builders, who understand this stuff, think about their end users, because they’re the people this tech is ultimately gonna trickle down to.
\ Finally, never be afraid to change your mind. It’s okay to be wrong sometimes. And it’s more than okay to change your perspective on something when new evidence comes to light, which, in an industry evolving as fast as AI, is a given. Be confident in your convictions, yet honest enough to admit that you will always be a student and will always be learning. Your team and your shareholders will respect you a lot more if you do. Move fast, learn faster, and try not to break too much stuff along the way.
\ Don’t forget to like and share the story!
:::tip This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO
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