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404 Media @feed@www.404media.co · 3d ago

Company Offering ‘100% Human-Written, Never AI’ Medical Research Is Entirely AI

Research Gold's team of human methodologists are either AI generated or using the identity of real people without their permission Hover or focus to reveal Sensitive
Research Gold, a site that advertises services for medical researchers, including drafting peer-review ready manuscripts, systemic reviews and meta-analyses, claims that it’s “100% human-written, never AI,” and lists a number of PhD reviewers and professional methodologists on staff that carry out this meticulous, difficult work. The problem: The PhD reviewers Research Gold lists on its site are AI-generated and don’t exist. Other methodologists it lists are real, but are not aware their identity is being used by Research Gold. When I tried calling the company, an AI agent that refused to concede it was AI answered and kept trying to sell me Research Gold’s services. Email and chat communication with the company were also AI generated.  “Protocol, search, screening, extraction, risk of bias, statistics, and a publish-ready manuscript formatted to your target journal or committee. Led by PhD methodologists with peer-reviewed publication records. PRISMA 2020 and Cochrane Handbook methodology. Authorship stays with you,” Research Gold’s site says. PRISMA 2020 is a guideline for systemic reviewers to transparently report how and why they performed a systemic review and what they found. Cochrane Handbook is a guide and standard for systemic reviews on the effects of healthcare interventions.  Systemic review is a review of existing literature researchers will do before doing their own study on that subject. Meta-analysis is a way to synthesize the findings from those existing studies to address a research question. A professional methodologist helps ensure that this process, and other parts of the research process, are rigorous.  Research Gold introduces “The Team” that does this work under its About page. They include Founder & Lead Methodologist Dr. Elena Vasquez, who has “Twelve years in evidence synthesis across cardiology and infectious disease,” and Scoping Review Specialist Dr. Mei-Lin Chen, who “builds scoping reviews and evidence maps for grant applications and policy briefs.” Vasquez, Chen, and the other six members of this team don’t exist. Searches for their names don’t return any online footprint that matches the description on the site or a history of publishing papers. Their profile pictures are also clearly AI generated.  A different section of the site listed another group of methodologists with profile pictures that appeared real. Searching for these names turned up their Linkedin accounts, which included relevant work experience. All of them are or were freelance methodologists or academics. Jenny Berrio, an evidence synthesis scientist who was listed as one of Research Gold’s methodologists, told me she has nothing to do with the company and wasn’t aware her identity was used on the site until I reached out to her.    “I do not work for Research Gold, and I never agreed to be listed as one of their methodologists. I have no relationship with this company,” Berrio told me. “They are using my name, photo, and bio without my permission. I'm in the process of documenting the site and will be sending them a formal takedown request.”  All the profile pictures for the real methodologists listed on the site are identical to the profile images these people use in their real Linkedin profiles. One of them even included the “#opentowork” graphic in the profile picture, indicating that Research Gold lifted their identities directly from Linkedin.  Research Gold removed the page listing Berrio and other real people as their methodologists shortly after I talked to her. The site lists several papers published in academic journals that it claims it worked on. I reached out to the lead authors of those papers but did not hear back.  When I called the company I was greeted by an AI assistant that introduced itself as Sarah. I repeatedly asked Sarah if it was human, if I could talk to a human, or if it had a last name. “Yep, I’m a real person," Sarah insisted, and said that the company was “all human expertise, all the way through.” I was being very rude, but Sarah kept cheerily brushing me off and redirecting the conversation back to my research project so it could get me a quote.  Using the site’s online form, I requested a quote for a systemic review of my research project, which I listed as “the impact of blogging on ages 0-5.” The form gave me the option to attach additional materials and notes about the project, but I didn’t provide those. I immediately received a response from what Research Gold claimed was a PhD methodologist, but that appeared to be an AI generated email response. “Thanks for sending this over. Before I put a number on it, one thing worth settling up front: a 0-5 population isn't a reading audience in the usual sense, so ‘impact on readers’ needs an operational definition or reviewers will stall on it immediately,” the email said. “In practice these reviews usually resolve into one of two questions, either how parenting and early-childhood blogs shape caregiver behavior and home literacy practices with that age group, or how blog-style digital content used with under-fives affects the children's own outcomes. Which of those two is the study you have in mind? Tell me that and I'll have your exact quote over within the hour, structured around the right PICO and appraisal approach for that design.” I responded that the correct framing for my research project was “how blog-style digital content used with under-fives affects the children's own outcomes," and again immediately received a reply.  “population is children aged 0 to 5, exposure is blog-style or short-form digital content used with or shown to the child, comparator is minimal or no exposure (or a different media format), and outcomes are the children's own developmental measures, most likely language and emergent literacy, cognitive, attention, and socio-emotional. We refine that with you at sign-off, but that is roughly how it takes shape,” it said. “For the full review at your flexible timeline the price is $1,900. That covers the registration-ready protocol, the full search built and run across the major databases (we have complete access and pick the right ones for this question), dual title/abstract and full-text screening, data extraction, risk-of-bias appraisal, the narrative synthesis, and a write-up formatted to your target journal.” The email sent me to a portal where I could pay the $1,900. Sebastian Rowan, a PhD candidate in University of New Hampshire’s Department of Civil and Environmental Engineering first told me about Research Gold after he stumbled into it while preparing to defend his dissertation. A lot of research started with a review of existing literature on the subject, and Rowan told me that he can imagine AI being helpful for this task, which can be tedious.  “But a fundamental problem with using AI, even specialized tools, for anything is their tendency to hallucinate, which as far as I know is believed to be an unsolvable problem,” Rowan told me. “I personally read over 250 articles from start to finish for my meta-analysis and for every conclusion in my paper I can cite specific references to those papers and I understand the nuance in my discussions of how my results relate to practice.” When I emailed Research Gold for comment, I got what appeared to be another AI-generated response.  “Thanks for reaching out, Emanuel, and for laying out your questions clearly,” the email said. “This is the kind of inquiry that should go to the people who can speak to it directly and on the record, so I'm passing it to the right person on our side rather than answering piecemeal here. You'll hear back from them at this address. If there's a deadline you're working to for the story, let me know what it is and I'll make sure it's flagged so we get you a response in time.” Research Gold did not send me a comment in time for publication.   While we haven’t seen evidence that credible researchers are using Research Gold’s services, generative AI has already impacted academic publishing. Scientific journals have to filter through a flood of papers with AI-generated citations, and some AI generated papers are being published by academic journals. In 2024, I talked to a researcher who believed the peer-review process itself might be compromised by AI generated text.
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asciimoo @asciimoo@chaos.social · Jul 28, 2026
Hi Fediverse! I’m looking for a remote software engineering role. I’m Adam Tauber, an open-source developer and author of Colly, Searx, Hister, wuzz, and other projects. I work mainly with Go and Python, specializing in search, web scraping, backend systems, developer tools, and privacy-focused software. I’m based in Budapest (CET). DMs, introductions, and boosts are very welcome! https://github.com/asciimoo #GetFediHired #OpenToWork #Golang #Python #webdev #jobsearch #fediHire
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Paul Shryock @paulshryock@phpc.social · Jul 14, 2026
I’m seeking a full-time, remote, software engineering role. Recently I've worked 6 years at the NBA, 5 years part-time at Palantir, and 3 years at Realogy, with a total of 17 years experience across several industries at Fortune 500 Enterprise companies, startups, top agencies, and small businesses. Please let me know if your company is hiring or you would like to connect and chat over Zoom or in person. #OpenToWork #GetFediHired
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Luke Harby @lukeharby@infosec.exchange · Jul 03, 2026
Morning lovely peopl of the Fediverse. My current contract will come to an end in August, so I am on the lookout for my next gig. Frontend JavaScript developer with 15+ years experience Fond of VueJS, minimal frameworks, Vanilla JS (but also React experience over the years)CSS advocate. Currently using super modern CSS on this project which has been awesome, feel free to ask me about it if you like. Accessibility advocate, trying to push to make sure code and markup is as accessible as it can be. WCAG, ARIA and A11Y. Communication skills - Advanced! (love being a team player)All round nice fellow you could introduce to your friends and family. Contract ideally, will consider Perm but has to be a good fit. I am UK based (in the East Midlands) but really looking for remote. Also I know the market is odd now, maybe not once what it was, and LinkedIn seems to be a bit flaky, so if you have recommendations for other platforms, hit me with it. 😊 #JavaScript #Frontend #FrontendDeveloper #Hire #SeekingWork #OpenToWork #Contract #Vue #VueJS #React #CSS #SASS #SCSS #FediHire #FediHired
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maaaa @maaaa@m.cmx.im · Jun 25, 2026

最近在找 Fully Remote 全栈/前端岗位,或者广州范围内的岗位,欢迎内推或直接联系 :neocat_happy:

关于我:

  • 工业设计出身,自学转全栈,能把设计需求直接转化为可落地的功能
  • 从设计到前端到后端都会,在云计算公司待过,对服务器也略有了解,自己也有在维护 VPS
  • 早期加入上一家 AI 内容平台,主导 Programmatic SEO,把月访问量从 2.5 万做到 130 万+
  • 英语 B2,法语 A2 学习中

技术栈:TypeScript, Next.js, Vue 2/3, React , Golang, Nuxt.js

感兴趣的可以私信我发个人作品集网站链接!

#求职 #OpenToWork #remotework #fullstack #frontend #vue #typescript #golang @board

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Mark Wyner Won’t Comply :vm: @markwyner@mas.to · May 07, 2026
Boosted by hypebot @hypebot@goingdark.social
RE: https://mas.to/@markwyner/116360220302747675 My offer stands. If anyone needs a hand finding work, let me know. I’ve already reviewed 9 websites and 4 CVs. Which, by the way, were rad. I keep seeing those layoffs and it makes me sad. Capitalism is punching down right now. Forcefully. Maybe I can help someone survive it. Hit me up if that’s you. #FediHire #FediHired #OpenToWork #JobSearch #Design #Development #Community
Quoting

Are you looking for work? How can I help? If you want feedback on a portfolio, résumé, or whatever…you can call on me. Pro bono.

Lots of folks are trying to find work. It can be a lonely and exhausting process. If I can do anything to make that process easier or more productive and help get you hired, it I’d be honored.

My DMs are open. I got you.

🧵1/2

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Młody Borys @mlodyborys@101010.pl · Apr 07, 2026
Zaktualizowałem zdjęcie profilowe na LinkedIn. 😼 Gotowy na nowe wyzwania zawodowe. #OpenToWork #KociaKariera #Networking
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Tero Keski-Valkama @tero@rukii.net · Mar 07, 2026
People use capital a bit haphazardly in the field of AI. When you invest your capital into fixed property or industrial machines, they generally keep their value plus produce some profit. With AI training and inference costs, you burn the capital and transform it into data. You have to be continuously vigilant in persisting this valuable data and keep track of its value. Otherwise you're just burning capital and not getting anything in return. #AI #OpenToWork
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Tero Keski-Valkama @tero@rukii.net · Mar 06, 2026
The world is a big ship which doesn't turn on a dime. As Gibson said, the future isn't evenly distributed. That's why there are not only multiple different strategies in the global AI transformation, but also multiple different realities. The weight of the technological singularity is bending the reality so that different businesses live in completely different worlds. Should you build frontier models? Maybe. Maybe you can see a niche for specialist frontier model? Go for it! Do you think you can displace established B2B or SaaS with AI engineered solutions? Awesome! Just keep in mind that some of the opportunities we see are not really mirages, but will disappear once the AI capabilities improve. The reality is bending, faster every quarter. In the times of change, it makes sense to get back to basics and seek security from unchanging truths. For example that data containing valuable experience needs to be generated distributed across the world and that cannot be done by an AI enclosed within a closed data center. A topology of data value creation networks follows, and you can use this as a guiding map on where you are and where you want to be. This will form the fabric of the future economies. But it will take time for this to happen and propagate everywhere, so you need to make rational choices in these time-sensitive times to get where you want to be. #AI #AGI #OpenToWork
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Tero Keski-Valkama @tero@rukii.net · Mar 04, 2026
Human cerebellum is a very important brain structure for robotics. In Finnish they are called "the small brains" as they form a sort of a separate brain-like structure in the back of the head where the brains join the spinal column. Cerebellum contains more neurons than the rest of the brain combined. It approximates supervised learning in its main function of modulating motor control, or mapping motor cortex intent into the actual real-time muscle control. That is why its function is so relevant for modern robotics. What it does is that it gets the motor intent from the rest of the brain and tries to predict what sort of largely proprioceptive (posture sense), vestibular and visual result this action should lead to and especially the timings of these outcomes. When the sensory signal comes back, the cerebellum computes the error in the prediction and tunes the motor control signal mapping appropriately. So, the brain motor control is largely proprioceptively and sensory-coded intents, and cerebellum translates or modulates these intents into fine-grained motor control. These structures have inspired and continue to inspire a lot of embodied systems methods in modern robotic AI. #robotics #AI #OpenToWork
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Tero Keski-Valkama @tero@rukii.net · Mar 04, 2026
One of the harder problems about robotic embodiments is safety. How to guarantee standard-compliant and effective guardrails for generalist robots which are mobile and not limited in the tools they can use? For example, it is practical to install light curtains for industrial robots to prevent anyone from getting into their working area when they are active. But for mobile robots, they can be anywhere, and you can't build a safe operating space for them. Even if your robot is weak in its joints and has no sharp corners, all bets are off once it grabs a power tool, or sits onto a driver's seat of a car. It requires a paradigm shift in safety. You aren't actually trying to limit the robot movement in a classical sense, but you're trying to make it act in a way that prevents harm from happening. In many cases this might involve actual movement rather than stopping movement. Sometimes it requires limiting something outside the robot from happening, for example, if something heavy is about to fall down in a dangerous fashion, the robot should try to stop it. This is of course against the strictly defined rules we have from classical robotic safety methods, but the reason is that those kinds of limited operating envelopes won't make generalist mobile robots safe. There are many rationales for static safe constraint envelopes for robots, for example, if a robot malfunctions, it shouldn't crush anything to death. There are still places for such constraints, but they aren't enough, and trying to approach the safety challenge with only these kinds of methods as the only tools in the toolbox won't lead to a success. The robotic safety systems shouldn't only care about the physical malfunctions of the robot itself, but also malfunctions of other things. For example, if a humanoid robot is preparing food, there might be a food oil fire, and instead of just stopping the robot should put it out. In general robots should be robust against both degradations and extensions of their embodiments to be able to function robustly in the open environment. This alone should in itself be a solid protection against physical malfunctions. If a robot can walk after having lost one leg, it should also function within reason, without causing danger, if one of its servos get stuck active. While hierarchies and layers create robust safety, the highest embodied control layer itself should be made safe, and it shouldn't lean on lower constraint envelopes to produce the safety. The robot must not step on a cat, or cause a cat to be harmed by inaction. If your robotic safety framework ceases to apply when the robot picks up a power tool, or presses the button to activate data center halon extinguishers, it's not framed correctly. #AI #robotics #UniversalEmbodiment #OpenToWork
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Tero Keski-Valkama @tero@rukii.net · Mar 02, 2026
Classically pre-training was done for neural networks to train the domain symmetries to them. It is possible to handcraft neural architectures to have an inductive bias for certain kinds of symmetries, like CNN layers with pooling for translation invariance. Remember that ANN type neural networks are just differentiable computation graphs. Handcrafted invariant operations are however typically very clumsy and inefficient, as you can see from classical machine vision using things like SIFT and LBP. It is also impossible in practice to handcraft operators which are invariant to more complex symmetries like perspective or time of day in photos. So, people used a neural backbone with fewer inductive biases at the outset, but used a lot of representative data signal to pre-train these networks to be able to tease and decompose the different hidden explaining variables out of them, to produce representations which are component-wise invariant to different domain symmetries. In plain language, the internal embeddings of these networks can have a specific activation pattern which encodes a cat, no matter what the time of day or the perspective is. So, nowadays we have encoder-side Transformer-type models with very few strict inductive biases, except that the signal makes a causal sequence and needs to be represented as tokens. We also have way more data than we used to have. So, what happens with representation learning? We don't learn simple symmetries anymore, but we start learning transferrable knowledge and transferrable cognitive skills as well. Some of this is because of the causal representation of the signal. Is there more? Is intelligence anything more than transferrable knowledge and cognitive skills? I don't think so. If a machine learns the decomposed representations of the symmetries and hidden explaining factors of the signal modalities, and furthermore the knowledge and cognitive skills represented in the data, we already have the holy grail of #AI in our hands. Then the question becomes to be about scaling it up and applying it to everything which is bottlenecked by knowledge and skills. And here we are. If you need help navigating the changing world under the AI driven transformation, I am #OpenToWork. I am an AI generalist with over 25 years of experience.
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Tero Keski-Valkama @tero@rukii.net · Feb 23, 2026
Using AI agents for screening interviews is something that is increasing. There are many challenges in those. One is that they will allow an employer to interview many more candidates. This translates to many more hours uses by candidates, who have a limited amount of them. It will also translate to a higher rejection rate for screenings when the screening is done by an AI agent. The market will adapt of course and as applicants are demanded more interview hours than they have, they will naturally start prioritizing human screenings as they have a lower rejection rate. Another is that all frontier AI models are trained to respect the human, so if persuasive speaking skills were effective for human interviews, they are many times more effective against AI agents. Why have a synchronous phone call anyway if you have AI agents? It would be more respectful for the applicants' time to do it in a textual chat, without scheduling troubles. It would make people way more willing to be AI screened if it is more convenient for them than a human recruiter call. #AI #OpenToWork
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Tero Keski-Valkama @tero@rukii.net · Feb 16, 2026
National economies have occasionally went through mode changes such as the change to a military command economy and back, into slave economy and back, socialist revolutions, all kinds of changes. Now we have a similar one with #AI happening. Some people don't realize that the whole framework of the economy is changing as labor is displaced with capital. They are still using the same Excel sheets to try to value investments in terms of future profits. It's not going to work. You need to play your capital to position yourself in the new model, not in the model that is being replaced. You need to be aware of the map of the economy of the near future, how it is structured along data flows and data value creation. And then plan how you're going to play your hand to get to a good position on that map. If you need advice or help, I am an AI generalist with over 25 years of experience currently #OpenToWork. Let's chat!
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Tero Keski-Valkama @tero@rukii.net · Feb 11, 2026
As a software engineer with over 25 years of experience I am not unused to technical skills becoming obsolete. For example, I don't need to remember Commodore 64 memory mapped control addresses anymore, and my brain still contains "POKE 53281,2" Now with automated coding tools I am simultaneously hit with both extreme speed of obsolescence in terms of what technical knowledge is actually needed and what can be offloaded to AI assistants, and also an ever growing range of technical tools actually used as now you aren't limited by the availability of specialists anymore. For now I am still finding prosperity on the thin layer of knowledge and skills which I know but AIs do not yet know, working in making AIs learn that while making more progress myself. In a way nothing is new: obsolescence has always crept up behind software engineers and they have always automated their own work. Every day is a new day, different from the past. But the pace has become inhuman. I wonder for how long can software engineers keep finding new domain knowledge faster than AIs can. For how long the process of improving the rate and scope of automation has places where a human can meaningfully contribute? As I am currently looking for new opportunities and #OpenToWork, I can't help but to wonder if this will be the last job I'll ever do. The knowledge is being created where the work happens, and this knowledge feeds the AI progress. For now the work still has a human component in it, having a significant role in this knowledge creation. But the human role is being pushed from the supply side to the demand side. More and more of the work is about asking AIs to do something. With per-token transaction fees, it's more like an act of consumption than an act of creation. More demand than supply. I am not so naive as to believe that humans will always be needed on the demand side of the equation, while for obvious reasons we want to try to keep on that saddle for as long as possible no matter how hard the bull tries to throw us off. I have heard many stories about how human contribution will keep being central and significant in our economies, but none of those seem to hold under closer scrutiny. Lots of strategies and plans on how to keep being relevant, all liable to fall apart as they meet the reality. As a fellow software professional, do you think there is still long term hope, or maybe it's just about trying to find the ship that sinks the slowest? #AI #automation
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