Godox spent years building lights that help photographers shape what they see. The company’s first camera flips the usual relationship between photographer and device. Instead of a bright rear screen that pulls attention away from the scene, the C100 gives you a clear window you compose through while key information floats on the glass itself.

The C100 camera’s body is 104 x 72 x 19 millimeters and weighs 65 grams, making it light enough to fit easily into a shirt pocket or be attached to a strap like a tiny accessory. The front panel is dominated by a sleek 60.8 x 47.8 millimeter transparent window that allows more than 50% of light to pass through, allowing you to see the real world directly in front of the lens. At the same time, the panel displays some extremely helpful information, like frame lines, exposure data, and battery level, without having to resort to a video feed and muck up the scene.

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Changing the aspect ratio brings the frame lines on the panel up to speed in real time, and you can select from a variety of settings such as 16:9, 4:3, 3:2, and 1:1. The center-weighted meter performs an excellent job of reading the scene and provides a wealth of helpful information, including ISO, aperture, and shutter settings ranging from 100 to 800 in manual steps. Aperture and shutter are linked in ranges from f/1.0 to f/64 and 1/8000 second to 1 second, so as you change one, the other is suggested in the background, which is quite useful if you’re trying to get everything just right before putting up any real film elsewhere.

The controls are as simple as you’d imagine, with only a few of arrow keys and a select button on the back for menu navigation and settings, and the shutter is ready to go with a quick click. There is no standard screen to see the photos you’ve just taken; the image is saved directly to the microSD card as soon as you press the shutter. Once it’s there, you may use USB-C to transfer content to your phone or computer, or you can just remove the card.

Godox claims that the C100 was created to be an all-day camera that allows you to get to the core of the moment with no worry or hassle, as it’s all about seeing and feeling first, then clicking the shutter, and, of course, analyzing your images afterward, when you’re feeling more relaxed. They believe that the wait between snapping the shot and viewing the outcome will make each snapshot feel like a small surprise when you open the files. The actual resolution of the images appears to be on the low side, ranging from 320 to 570 kilobytes, so don’t expect to be making any large prints very soon. Oh, and the camera also shoots video, though the exact specifications are not yet available.

At 199 yuan (or roughly 29 USD), the C100 is clearly in the same category as some of the other recent basic cameras that are all about providing an experience rather than just a set of specifications. It’s a step up from some earlier transparent-window cameras because it has live data overlays rather than static printed graphics, and its creators see it as an ideal companion for film photographers looking for a lightweight metering and framing tool that will fit nicely in their bag alongside their medium-format body.
[Source]

Concerns over the impact of AI chatbots on mental health, personal safety, harassment, and disinformation have forced AI developers to implement safeguards to better control how and what their AI models are allowed to respond or do.

But concerns and worries can’t erode demand. AI offers a lot of promise, and people don’t want a faceless tech company to restrict their access to that potential. And if they can preserve their privacy while they use AI models however they want, why not?

Venice AI, which offers access to more than 200 AI models while allowing users to retain their privacy, is raking it in thanks to that demand. Just two years in, the company already has more than 850,000 unique visitors to its website, and serves more than 3 million active users and an average of 1.7 million API calls per day.

The startup hosts “uncensored,” open source models on its own data centers, and routes queries to closed-source models, such as those by OpenAI or Anthropic. All user input is encrypted and unencrypted client-side, and routed through an external proxy before it is processed and returned, with no data stored on Venice’s own systems. It also provides end-to-end encryption on some models, though you have to pay for a subscription to get that feature.

The company is already profitable, with annualized run-rate revenues of over $70 million, its CEO Erik Voorhees (pictured above, in the center) told TechCrunch during an exclusive interview.

Understandably, investors have flocked to get a piece of that traction. Venice AI on Wednesday said it had raised a $65 million Series A at a $1 billion valuation, its first external fundraise. The round was led by crypto-focused venture firm Dragonfly, with participation from Coinbase Ventures, North Island Ventures, and others.

The overlap between Voorhees, Venice’s focus on privacy, and its new crypto investors is hard to miss, especially given the CEO’s background and past work. An early bitcoin advocate, Voorhees has founded a few crypto companies, including bitcoin gambling site Satoshi Dice and cryptocurrency exchange ShapeShift, and has long advocated in favor of preserving users’ privacy.

In fact, when a Wall Street Journal investigation accused ShapeShift, which initially didn’t require its users to identify themselves, of processing millions of suspect funds, Voorhees reportedly said: “I don’t think people should have their identity recorded to catch an occasional criminal.”

He struck a similar note when asked how Venice AI thinks about offering access to AI models in light of recent cases of AI psychosis and resulting harm, saying his team treats their service as a “neutral tool or a neutral platform.”

“This is the same principle that you have in Bitcoin, where Bitcoin, as a neutral protocol, works the same way for all people,” he said. “I think it’s actually quite dangerous from a safety perspective, for the world to enter this next phase and have everyone be constantly watched. To me that is actually much more dangerous than any particular person asking a controversial question or something that might be considered bad.”

There’s a considerable focus on giving users agency, too. Users can freely choose from AI models that can generate text, images, audio, and video — all of which vary in their performance, quality, and the amount of censorship applied. The website prominently features several AI “characters” that you can customize and chat with, and the company proudly states it offers an “uncensored” experience.

“We’re optimizing for freedom and actually respecting users as adults, which is, I think, rare these days,” Voorhees said.

The founder said Venice also works on some open models’ system prompts to instruct them to answer more openly, though it doesn’t add any restrictions to the models.

Unsurprisingly, there are two crypto tokens associated with the effort. Venice launched a token called “VVV” in early January, in a bid to attract users, Voorhees said, and in August last year added another, called “DIEM.” Users can buy VVV and then stake it to mint DIEM, which generates $1 worth of AI credits per day that you can spend on Venice. However, Voorhees said only about 8% of the company’s users pay with crypto.

The founder credited the company’s growth to the good performance of the crypto tokens, though he said the strongest driver was getting close to feature parity with ChatGPT. “When we launched, we were very far away from what ChatGPT could do, but people would use us because it was private. And today, we’re very close to what ChatGPT can do […] so as we’ve closed that gap, it’s become an increasingly compelling alternative,” he said.

Looking forward, Venice AI wants to use the fresh cash to start buying GPUs and building its own data centers so it can stop leasing GPUs and increase its gross margins.

Manual transcription takes much time that most people do not have. I usually spend hours every week turning interviews and meetings into text, so I tried Transkriptor through a test to see if it could take notes and save me time. Transkriptor is an AI speech-to-text tool. It converts audio and video files into editable transcripts, and it supports 100+ languages. Over a week, I uploaded my clean and slightly messy recordings, ran them against accented audio, and also linked them to Zoom and Google Meet calls.

Here is how Transkriptor does well, where it goes wrong, and who should use it. 

How Do You Get Started With Transkriptor?

To start with Transkriptor, it does not take more than 1 minute. You can sign up with Google, Microsoft, Apple, or email. Transkriptor leans on a row of recognizable logos, from Pfizer and Tesla to Harvard and Microsoft, to build your trust before using it.

Transkriptor offers a clean, easy-to-navigate dashboard with 5 ways to create a transcript. You can record live audio, upload a file, pull a video from YouTube, join a meeting, and import audio-video files from the cloud. A left rail holds the heavier tools, including text-to-speech, AI content generation, and a calendar for scheduled meetings. The core action is never more than one click; you get transcription without any technical difficulties.

How Accurate is Transkriptor at Converting Speech to Text?

Accuracy is the most important thing, and an honest answer is better than a flawless one. On clean English audio, meaning a single speaker in a quiet room, Transkriptor landed in the high-80s to low-90s percent range in my tests, which matches what independent reviewers report. If you upload a clean 30-minute file, it will take you only a few minutes to check for grammar mistakes, mostly punctuation marks.

I started testing the tool by uploading different audio and video files, and Transkriptor supports a wide range of formats, so I never had to convert the file before uploading.

Audio with background noise and overlapping speakers leads to less accurate transcription. Also, non-native heavy accents reduced accuracy. Transkriptor supports 100+ languages and adds domain-specific vocabulary for medical, legal, and IT terms, which helped with a jargon-heavy recording, though non-English audio was less even than English.

Transkriptor’s editor did the real work. Every line of transcription carries a timestamp and speaker label. You can play back the audio while reading the transcription to ensure everything is up to the point. Additionally, AI chat and summary let you pull a quick recap of the whole conversation. You get richer insights, such as sentiment analysis and speaker talk time, but it is locked behind the Team plan.

Does Transkriptor Handle Zoom, Google Meet, and Teams Meetings?

Yes, Transkriptor handles Zoom, Google Meet, and Teams meetings with ease. You paste the link to add a recording bot to the live call. Or you can connect your Google or Outlook calendar so Transkriptor auto-joins scheduled meetings. I connected my Google Calendar in 2 clicks and set it to auto-detect the platform and record the meeting.

After each call, I got a transcript with speaker labels and an auto-generated summary with action items, which is exactly what a remote team wants from a note-taker. The bot-joins-the-call model is the same approach Otter uses, and Transkriptor matches it while supporting far more languages.

What Does Transkriptor Cost, and How Does It Compare to Otter and Sonix?

To get access to all features, you need to buy a Transkriptor subscription. It’s a limited free tier with a small daily allowance that lets you test it. Lite plan starts at $9.99 per month for 5 hours of transcription. Pro is $19.99 per month or $8.33 per month on annual billing ($99.99 a year) and unlocks 2,400 minutes per month with unlimited files. 

Team runs $30 per seat monthly, or $20 per seat on annual billing ($240 a year per seat), adding 3,000 minutes per seat, shared workspaces, call analysis, and custom vocabulary. A custom-priced Business tier is available for larger orgs. Transkriptor is also ISO 27001, SOC 2, and GDPR compliant, which matters for regulated work.

Against the alternative transcription tools, Transkriptor lands in a useful middle ground. Otter is the polished meeting assistant with strong CRM sync, but it transcribes only 6 languages and caps your minutes. Sonix charges per hour and delivers the highest audio accuracy. Here is how the three line up.

Who is Transkriptor Best For?

With Transkriptor, you get a practical mix of transcription, meeting recording, AI summaries, and multilingual support. During my testing, Transkriptor handled clean audio and video files well and integrated smoothly with Zoom, Google Meet, and Teams. It made it easy to turn speech into readable meeting notes and summaries.

While accuracy can vary with heavy background noise or challenging accents, the overall experience is reliable enough for most everyday transcription needs. The combination of 100+ language support, meeting integrations, and competitive pricing gives it a broader feature set than many alternatives.

For students, journalists, podcasters, and remote teams working across multiple languages, Transkriptor is a capable and cost-effective speech-to-text solution.

Local area networks (LANs) that use technologies like Ethernet and Wi-Fi are incredibly useful for letting devices talk with each other. Yet a core problem here is knowing which devices are where on the network, as anyone who has ever tried to add a network printer or network share to their system can probably attest to. Unless you happen to know the IP address of the LAN device, the port, and protocol, the target device may as well be located on the Moon without further help, such as automatic network discovery in lieu of waddling over to the device and reading the label listing its IP address.

Over the decades quite a few ways have been developed to enable such network discovery, with many of them using UDP broadcast as the first step. By broadcasting a global message on the entire LAN, any device that has an actively listening UDP socket on that particular port can parse said message and decide whether it’s feeling sociable enough to reply.

The topic of UDP broadcasting is however not as straightforward as it may sound if you’re just getting started, including the existence of many opinions on the ‘right way’. There is also a massive divide between a sprawling service discovery protocol like mDNS and a light-weight one like that one that I had to implement a few years ago for an open source project.

Network Broadcasting

The obvious advantage of a broadcast message is that a client device that seeks its protocol soul mate on the LAN doesn’t need to ping all possible IP address and subnets. Instead,  a broadcast message is designed so that all connected networking devices know that it should be forwarded to all other known devices. Thus with a single message from the client, in theory, only a single message will then neatly land at every single other connected system.

Of course, this ignores happy joy fun things such as convoluted network configurations, such as those involving overlapping Wi-Fi repeaters and subsequent routing, but in general we can assume that this is how it works. Various edge cases and fascinating complications of these will be considered in a later section.

Much of this service auto-discovery is tossed under the header of ‘zero-configuration networking‘, or zeroconf for people who don’t like typing. The best part about zeroconf is probably that there are so many standards here, ranging from DNS-SD to mDNS, UPnP, SLP and others. Perhaps unsurprisingly, one of the major issues here is that platform support here is spotty, with mDNS – despite being one of the most universal – not having much support outside of MacOS/OS X with Bonjour and Linux/BSD with Avahi.

Thus while trying to add the auto-discovery of NymphCast receivers and media servers by NymphCast clients, I found myself asking the daunting question of whether I was at risk of being about to embark on reinventing the proverbial wheel. After all, nobody wants to become the subject of an xkcd comic.

UDP Discovery Basics

As it turns out, I ought not to have been too worried, as despite looking everywhere I could find nothing along the lines of the NyanSD network service discovery (NSD) protocol that I ended up implementing and integrating into NymphCast. What I wanted after all was the most no-frills NSD possible that could be easily integrated, while working the same across just about any desktop, server and embedded platform imaginable.

All that’s needed for this is a way to create an appropriate UDP socket, and a way to either broadcast a query and receive the response, or to listen for incoming UDP packets. Here you can figure out the platform-native method for each target platform, or not reinvent the wheel and use an existing networking library for C++ like Poco. This is what I used for NyanSD, along with my ByteBauble utility to handle endianness conversions.

For the UDP server — the listening side — the procedure is fairly standard, with a regular UDP listening socket. As UDP is a connectionless protocol, there is not a lot of preamble here, just a UDP socket instance (here Poco::Net::DatagramSocket), which is bound to the target port and regularly polls for any fresh UDP packets to process. This can all be seen in the single source file for NyanSD which covers both the client and server side code.

Where things get spicy is with the client that sends the broadcast query and waits for any replies. If we were to just shove the query data into the socket along with the request to toss it over to a regular IP address, not a lot would happen. To make it into a broadcast request we need a few things:

1. Let the network subsystem know that we want to do broadcast things.
2. Create the special broadcast address for the target network interface.

With Poco the first point is easily handled by simply calling setBroadcast(true) on the UDP socket instance. For BSD sockets this sets the appropriate flag on the socket, which is essentially repeated across all OS implementations due to how prevalent the BSD socket library is.

The second point can be summarized for IPv4 as a curt ‘make it end with .255’. For example 192.168.0.255 when the client network interface’s IP address is 192.168.0.42. If there are multiple interfaces on the client system, you can go through the list one by one to broadcast on each of them before filtering out potential duplicate returns.

As for how to do broadcasting with IPv6: you don’t, as this protocol relies on multicast and special multicast receiver groups, which is another kettle of fish and of not much relevance for LANs.

Complications

If you look at the NyanSD API, it may give the impression that the query process is incredibly straightforward, with the sendQuery() function neatly returning a stack of remote systems that responded to our query. While these are definitely all the responses, it’s important to remember that NyanSD queries every single network interface. This means that the responses are likely to contain duplicates, which may even come from the loopback address when a service runs locally.

The filtering of this is captured in the NymphCast client library (libnymphcast) where the findServers() function in the main source file calls the isDuplicate() and isDuplicateName() functions, as well as the removeLoopback() function that nukes any responses that match a remote service found via a non-loopback interface. This last filtering is essential for NymphCast when e.g. using playback groups that would otherwise get confused by a stray loopback address.

Although one may think that such in-depth filtering is unnecessary if all you have is a single Wi-Fi or Ethernet interface in your system, one of the curveballs that I encountered during real-life testing was apparently related to Wi-Fi repeaters. For some reason it seems that the way that the repeaters did their broadcasting led to erroneous duplication of packets and thus multiple returns from a single system.

Depending on your exact use case and network configuration you may encounter any such issues and perhaps an exciting new one.

NyanSD Findings

Over the years that NyanSD has been used in the NymphCast project, it has proven to be one of the most reliable and probably nearly zero-fuss components. I have so far used it on Windows, various Linux distributions, FreeBSD, Haiku, Android, and the ESP32 via FreeRTOS and ESP-IDF. What this experience has proven to me most of all is that service discovery doesn’t have to be complicated.

The basic UDP protocol is simple and reliable enough that, barring a very sick LAN, there shouldn’t be any issues here. Assuming you get your filtering sorted of the responses, it’s probably the last part of a project to worry about.

One thing that I’m also very happy with in NyanSD is that there’s no set port in the protocol, like how mDNS always uses port 5353. What this means is that I can have NyanSD listen with a UDP socket on the same port as the NymphCast server’s TCP socket, which also means that different services with their own port can be targeted directly rather than every NyanSD-enabled service on the network getting blasted by every NyanSD query.

I did also do some work on a NyanSD daemon as a more central services database, but so far I have had no real need for it in a practical deployment. I guess that such a thing could be very useful if the port of a service is not set in stone, but generally that’s the one aspect of network services that tends to be boringly predictable.

Xbox and Nintendo have also been pushing consumers towards digital games.

from the you-got-boondoggle-in-my-boondoggle dept

Last fall, Ezra Klein was getting a lot of attention for his book Abundance, which basically argued that American had become bureaucracy-obsessed and fallen out of love with building things. I thought it was mostly simplistic cack, downplaying or ignoring the fact that the U.S. government has become so blisteringly corrupt, it clearly no longer functions in the public interest.

As a longtime telecom beat reporter I was particularly struck by Klein’s chapter on broadband, which mostly seemed to amplify Republican attacks. One of Klein’s biggest targets was the infrastructure bill and Broadband, Equity, Deployment, and Access (BEAD) program, which was part of the 2021 infrastructure bill, and set aside $42.5 billion for improved internet access.

BEAD was never going to be a poster child for government efficiency. But as I noted at the time, Klein’s criticism of the program was bizarre and simplistic, downplayed why the program was taking so long (we had to remap the entirety of U.S. internet access, for one), and ignored how other legislation that same year (like ARPA) was delivering much of the abundance Klein claimed to be looking for.

I could tell from reading Klein’s Abundance chapter on broadband that he didn’t spend much time talking to telecom policy experts. After Klein’s attacks made inroads on the podcast circuit (including on Jon Stewart’s) they were then picked up again by right wing media, further perpetuating the idea that BEAD was a completely useless boondoggle:

I bring it up because a little more than a year later and this BEAD program really is now a boondoggle under Trumpism, as Sean Gonsalves and I explored in a new feature over at The Verge.

Republicans, it should be noted, voted against the infrastructure bill and ARPA, but can still routinely be found taking credit for the improvements they opposed.

Last election season, Republicans ran on the idea that they’d reshape BEAD and trim the fat. Instead they’ve stripped away all oversight, eliminated any requirements that taxpayer-funded broadband be affordable or equitably deployed, and gone out of their way to redirect money away from future-proof fiber toward Elon Musk and Jeff Bezos low-Earth-orbit satellite broadband networks.

Republicans — and the Joe Rogan infotainment universe — are positively convinced that Starlink is akin to magic. So they’ve decided to throw billions of taxpayer money at Bezos and Musk in exchange for slower, more expensive, congested low-Earth orbit satellite connectivity that chips away at the ozone layer. It’s worth noting they’re being given billions for service that already exists and was already set to be deployed.

In our Verge piece, we talked to minority communities in Louisiana who were slated to get fiber upgrades, but are now being shoveled toward Starlink service (that already existed) thanks to Republican BEAD changes. They are very aware they’re now getting the short-end of the stick:

“The most frustrating part is that it was a zero dollar investment in infrastructure,” Wills told The Verge. “Nothing fundamentally changed. People with Starlink are going to just get mailed a box and many won’t be able to install it. And we still won’t have anybody really served,” leaving the community with “no growth in our economic potential.”

“No money will stay here,” he said. “No jobs will be created from this — no installation jobs, zero construction jobs, or even any small stimulus.”

Republicans are then claiming they “saved taxpayers money” by throwing money at billionaires for satellite broadband they already planned to deploy. States and the Trump administration are now bickering over these $20 billion in “non deployment funds.” Congress said this money had to be used for broadband access; but the law under Trumpism is very clearly optional. It’s a giant mess.

All of this corrupt retooling has caused endless new delays, pushing real-world deployments out by another year or two. As of this writing, the $42.5 billion program has only provided new (fixed wireless) connections to a handful of homes in Louisiana and Nebraska (the Trump administration tried to use this as a press op highlighting how amazingly successful their revamp has been).

Due to the higher costs of deployment created by stupid tariffs and pointless wars, many additional fiber deployment bids originally supposed to be funded by BEAD are likely to go into default and be cancelled, opening up the possibility of Musk and Bezos getting billions more in taxpayer subsidies. It’s expected that this whole mess will get significantly uglier later this year.

Curiously, Ezra Klein hasn’t made a peep. All the press coverage last election season about how BEAD was a boondoggle is nowhere to be found now that the program is a bigger boondoggle than ever. And it’s a bigger boondoggle than ever because the U.S. is too corrupt to function, something that needs to be addressed (and candidly acknowledged by our press) before we can even begin to sniff “abundance.”

I’ve always felt that the abundance movement was an influence campaign by affluent centrists to pre-empt genuine populist progressive reform as the response to authoritarianism. The abundance movement always struck me as Clinton-era vibes-based deregulatory corporatism with a new coat of paint; something seemingly supported by its proponents’ curiously limited attention span.

Filed Under: abundance, bead, broadband, donald trump, elon musk, ezra klein, high speed internet, internet access, jeff bezos, ntia, taxpayers, telecom

Companies: spacex, starlink

Cyber threat intelligence becomes more valuable when indicators are enriched with context that supports investigation, correlation, and decision-making. Through the Criminal IP integration with OpenCTI, security teams can transform IP addresses, domains, and URLs from isolated indicators into structured intelligence within the OpenCTI knowledge graph.

The integration automatically enriches indicators with Criminal IP’s reputation scoring, infrastructure intelligence, vulnerability data, behavioral signals, and phishing analysis.

The resulting information is structured as OpenCTI entities and relationships, allowing analysts to investigate connected infrastructure, identify potential attack surfaces, and prioritize high-risk indicators.

Integration Highlights

Contextual Risk Scoring Beyond Simple Reputation

Criminal IP provides dual-perspective risk scoring (inbound and outbound), reflecting both how an IP is targeted and how it behaves externally. This gives analysts a more nuanced signal than traditional single-score reputation models and improves prioritization of high-risk infrastructure.

Deep Infrastructure Intelligence Embedded in the Graph

Enrichment goes beyond tagging indicators, Criminal IP creates structured OpenCTI entities and relationships, including vulnerabilities (CVEs), Autonomous Systems (ISPs), and geolocation. This enables analysts to pivot across infrastructure, uncover shared components, and identify related infrastructure within the graph.

Service Exposure & Vulnerability Correlation

By linking observed services to known CVEs, the integration provides immediate insight into potential attack surfaces. Analysts can quickly assess whether an IP is not only malicious, but also exploitable or actively leveraged in attacks.

High-Fidelity Threat Labeling & Behavioral Signals

Automatically generated labels incorporate multiple data points such as anonymization technologies (VPN, proxy, TOR), hosting characteristics, and malicious classifications. This layered labeling approach provides richer context than binary “malicious/benign” tagging.

Advanced Domain & Phishing Intelligence

For domains, Criminal IP performs full URL analysis to detect phishing activity, credential harvesting, suspicious files, and impersonation techniques. Confidence scores are directly tied to phishing probability, giving analysts a quantifiable measure of risk.

Infrastructure Mapping & Analysis support

The integration links indicators to network ownership (Autonomous Systems), physical locations, and resolved IP infrastructure. This allows teams to identify hosting patterns, regional clustering, and and infrastructure patterns across indicators.

How Integration Works

Indicators such as IP addresses, domains, and URLs are first ingested into OpenCTI.

The Criminal IP connector then automatically enriches each indicator with reputation scoring, infrastructure intelligence, vulnerability information, behavioral signals, and phishing analysis.

The enriched data is structured into entities and relationships within the OpenCTI knowledge graph. Analysts can then use the resulting intelligence for investigation, correlation, infrastructure pivoting, and threat analysis.

The process can be summarized as follows:

1. Indicators (IP addresses, domains, URLs) are ingested into OpenCTI
2. The Criminal IP connector automatically enriches each indicator with reputation scoring, infrastructure intelligence, and phishing analysis
3. Enriched data is structured into entities and relationships, enabling investigation, correlation, and analysis within the OpenCTI knowledge graph

Key Use Cases

SOC Triage and Alert Validation

Rapidly validate suspicious IPs and domains using dual risk scoring, infrastructure context, and phishing intelligence, enabling analysts to prioritize high-risk indicators and support prioritization of high-risk indicators.

Threat Hunting and Infrastructure Pivoting

Leverage enriched relationships such as CVEs, Autonomous Systems, and geolocation to pivot across connected infrastructure and uncover related assets used in attacker operations.

Phishing and Campaign Analysis

Identify and analyze malicious domains, credential harvesting pages, and supporting infrastructure to track phishing activity and understand broader campaign patterns.

OpenCTI Platform

OpenCTI is an open-source cyber threat intelligence platform designed to structure, store, and analyze threat data using a graph-based model. It enables organizations to connect indicators, vulnerabilities, threat actors, and campaigns into a unified knowledge base for investigation, collaboration, and intelligence sharing.

Criminal IP

Criminal IP delivers decision-ready cyber threat intelligence by analyzing IP addresses, domains, and URLs across the global internet. Powered by AI and OSINT, it provides reputation scoring, infrastructure visibility, and real-time detection of malicious activity, including phishing, exposed services, and anonymization technologies such as VPNs and proxies. Its API-first architecture enables seamless integration into security platforms to enhance visibility, automation, and response.

Sponsored and written by Criminal IP.