18 October, 2024
SurrealDB is helping developers consolidate their databases
Brothers Tobie Morgan Hitchcock and Jaime Morgan Hitchcock spent years building cloud-based software-as-a-service systems together, ranging from tools to let golf courses measure “golfer engagement” to online platforms designed to assess job candidates. While the systems they built had wildly different functions, the unifying thread running through all of them was a dependence on databases that could allow the systems to grow while remaining performant.
Managing databases isn’t as challenging as it used to be. But at scale, it can be a different story — and Tobie says he and Jaime experienced many of the common pain points firsthand while building out SaaS systems.
“Developers spend excessive time on infrastructure management and ensuring that the data in their applications is consistent across multiple different databases and system types with different characteristics and guarantees,” Tobie told TechCrunch. “In addition to this, they need to learn new programming and query languages while working with many different client libraries, leaving less time for application development and leading to reduced performance and more complex application deployments.”
The Hitchcock brothers’ solution was a database architecture called SurrealDB, maintained by a startup of the same name Tobie and Jaime co-founded in 2015. (We covered SurrealDB’s seed in January 2023.) SurrealDB allows developers to model data using multiple different data models at once, and to deploy databases across both cloud and on-premises environments.
“As a multi-model database, SurrealDB helps organizations looking to consolidate the numbers of databases they own and manage,” Tobie said. “We believe that an important part of each database is the ability to easily and effortlessly control each aspect of the database.”
The database architecture SurrealDB, which was built in the programming language Rust, shares certain characteristics in common with relational databases. But it features added functionality like security controls and granular access permissions management.
SurrealDB also ships with a tool, Surrealist, that lets developers perform certain database management tasks visually, without having to write code.
SurrealDB the company is currently pre-revenue. However, the plan is to make money through Surreal Cloud, a fully managed version of SurrealDB that launched in beta early this month.
VCs have faith in the brothers’ roadmap. SurrealDB this week closed a $20 million funding round led by FirstMark and Georgian, with participation from Crew Capital and Alumni Ventures, bringing the 32-person, London-based startup’s total raised to $26 million.
Tobie says that the proceeds will go toward product development and “sustainable” hiring.
“SurrealDB has emerged as the choice for organizations burdened by the cost of managing multiple databases,” Tobie added optimistically. “With organizations looking to reduce the cost of the tech stacks, we see this as an opportunity for SurrealDB.”
SurrealDB is helping developers consolidate their databases
Brothers Tobie Morgan Hitchcock and Jaime Morgan Hitchcock spent years building cloud-based software-as-a-service systems together, ranging from tools to let golf courses measure “golfer engagement” to online platforms designed to assess job candidates. While the systems they built had wildly different functions, the unifying thread running through all of them was a dependence on databases that could allow the systems to grow while remaining performant.
Managing databases isn’t as challenging as it used to be. But at scale, it can be a different story — and Tobie says he and Jaime experienced many of the common pain points firsthand while building out SaaS systems.
“Developers spend excessive time on infrastructure management and ensuring that the data in their applications is consistent across multiple different databases and system types with different characteristics and guarantees,” Tobie told TechCrunch. “In addition to this, they need to learn new programming and query languages while working with many different client libraries, leaving less time for application development and leading to reduced performance and more complex application deployments.”
The Hitchcock brothers’ solution was a database architecture called SurrealDB, maintained by a startup of the same name Tobie and Jaime co-founded in 2015. (We covered SurrealDB’s seed in January 2023.) SurrealDB allows developers to model data using multiple different data models at once, and to deploy databases across both cloud and on-premises environments.
“As a multi-model database, SurrealDB helps organizations looking to consolidate the numbers of databases they own and manage,” Tobie said. “We believe that an important part of each database is the ability to easily and effortlessly control each aspect of the database.”
The database architecture SurrealDB, which was built in the programming language Rust, shares certain characteristics in common with relational databases. But it features added functionality like security controls and granular access permissions management.
SurrealDB also ships with a tool, Surrealist, that lets developers perform certain database management tasks visually, without having to write code.
SurrealDB the company is currently pre-revenue. However, the plan is to make money through Surreal Cloud, a fully managed version of SurrealDB that launched in beta early this month.
VCs have faith in the brothers’ roadmap. SurrealDB this week closed a $20 million funding round led by FirstMark and Georgian, with participation from Crew Capital and Alumni Ventures, bringing the 32-person, London-based startup’s total raised to $26 million.
Tobie says that the proceeds will go toward product development and “sustainable” hiring.
“SurrealDB has emerged as the choice for organizations burdened by the cost of managing multiple databases,” Tobie added optimistically. “With organizations looking to reduce the cost of the tech stacks, we see this as an opportunity for SurrealDB.”
SurrealDB is helping developers consolidate their databases
Brothers Tobie Morgan Hitchcock and Jaime Morgan Hitchcock spent years building cloud-based software-as-a-service systems together, ranging from tools to let golf courses measure “golfer engagement” to online platforms designed to assess job candidates. While the systems they built had wildly different functions, the unifying thread running through all of them was a dependence on databases that could allow the systems to grow while remaining performant.
Managing databases isn’t as challenging as it used to be. But at scale, it can be a different story — and Tobie says he and Jaime experienced many of the common pain points firsthand while building out SaaS systems.
“Developers spend excessive time on infrastructure management and ensuring that the data in their applications is consistent across multiple different databases and system types with different characteristics and guarantees,” Tobie told TechCrunch in an interview. “In addition to this, they need to learn new programming and query languages while working with many different client libraries, leaving less time for application development and leading to reduced performance and more complex application deployments.”
The Hitchcock brothers’ solution was a database architecture called SurrealDB, maintained by a startup of the same name Tobie and Jaime co-founded in 2015. (We covered SurrealDB’s seed last January.) SurrealDB allows developers to model data using multiple different data models at once, and to deploy databases across both cloud and on-premises environments.
“As a multi-model database, SurrealDB helps organizations looking to consolidate the numbers of databases they own and manage,” Tobie said. “We believe that an important part of each database is the ability to easily and effortlessly control each aspect of the database.”
The database architecture SurrealDB, which was built in the programming language Rust, shares certain characteristics in common with relational databases. But it features added functionality like security controls and granular access permissions management.
SurrealDB also ships with a tool, Surrealist, that lets developers perform certain database management tasks visually, without having to write code.
SurrealDB the company is currently pre-revenue. However, the plan is to make money through Surreal Cloud, a fully managed version of SurrealDB that launched in beta early this month.
VCs have faith in the brothers’ roadmap. SurrealDB this week closed a $20 million funding round led by FirstMark and Georgian with participation from Crew Capital and Alumni Ventures, bringing the 32-person, London-based startup’s total raised to $26 million.
Tobie says that the proceeds will go toward product development and “sustainable” hiring.
“SurrealDB has emerged as the choice for organizations burdened by the cost of managing multiple databases,” Tobie added optimistically. “With organizations looking to reduce the cost of the tech stacks, we see this as an opportunity for SurrealDB.”
Looking to retain App Store developers ahead of the DMA, Apple begins 'contingent pricing' pilot
Apple is moving to make the App Store more appealing to developers before the upcoming deadline to comply with the EU’s Digital Markets Act (DMA), which offers developers the ability to distribute apps through their own channels for the first time. In an effort to retain developers, Apple has begun its pilot tests of “contingent pricing,” a new way for them to market App Store subscriptions, TechCrunch has learned.
The feature, which Apple first announced last month, offers customers a discounted subscription as long as they’re actively subscribed to another subscription from either the same developers or two different developers. For individual developers who use it themselves, it would serve as a way to upsell to their existing, loyal customers by offering them a deal on another app in their portfolio.
Alternately, two developers could use the option to attract customers to their respective subscriptions — something that might make sense if the two apps offered integrations with one another or complemented each other in some way.
That’s the case with the debut pilot test of contingent pricing that pairs up two apps: Structured and one sec. The former is a daily planner offering a visual calendar and to-do list, while the latter is a productivity app aimed at breaking users’ social media habits by forcing them to pause before loading addictive apps. (An interesting choice for the pilot, given Apple and Meta’s ongoing beef over App Tracking Transparency, which Meta said harmed its business!)
For the tests, Apple chose the pairing, but both companies had been working together for a long time. In Structured, users can opt to block distracting apps during their unfinished tasks by leveraging one sec’s functionality. In addition, the apps’ founders, Frederik Riedel (one sec) and Leo Mehlig (Structured) had both received a scholarship to attend Apple’s developer conference, WWDC.
With contingent pricing, customers who subscribe to one of the apps can receive a discount if subscribing to the other. This deal is marketed in the App Store’s “Events & Offers” section, where it will feature the new, lower price ahead of the current subscription price, which is crossed out. This is followed by a note about the savings (e.g., “You are saving as Structured — Daily Planner Subscriber”).
The deal is advertised on the product pages of both apps, but Apple is also planning to market the discounts through separate placements in the App Store. The developers choose to advertise the offer on their own social media or websites, as well.
The pilot was launched on Thursday with these two apps, so it’s too soon to know if such offers will aid in conversions. However, it’s likely that a smart pairing where apps are integrated, as in this case, could do well. Apple says it helps the developers with the implementation of contingent pricing to make the redemption process “seamless” for customers purchasing through its App Store.
The pilot test’s launch follows Apple’s announcement this week of a slate of new rules specifically for app developers in the EU, which include reduced commissions, the ability to sideload apps, new security checks, as well as new fees. As a result of the increased competition Apple is sure to face in the EU, the company is in need of more ways to hook developers into staying on its App Store. Offering a program that could help them co-market their apps alongside other developers to increase subscription conversions could have some draw — and could potentially entice developers to stay on the App Store instead of setting up shop elsewhere.
Sentry's AI-powered Autofix helps developers quickly debug and fix their production code
Sentry has long helped developers monitor and debug their production code. Now, the company is adding some AI smarts to this process by launching AI Autofix, a new feature that uses all of the contextual data Sentry has about a company’s production environment to suggest fixes whenever an error occurs. While it’s called Autofix, this isn’t a completely automated system, something very few developers would be comfortable with. Instead, it is a human-in-the-loop tool that is “like having a junior developer ready to help on-demand,” as the company explains.
“Rather than thinking about the performance of your application — or your errors — from a system infrastructure perspective, we’re really trying to focus on evaluating it and helping you solve problems from a code-level perspective,” Sentry engineering manager Tillman Elser explained when I asked him how this new feature fits into the company’s overall product lineup.
Elser argued that many other AI-based coding tools are great for auto-completing code in the IDE, but since they don’t know about a company’s production environment, they can’t proactively look for issues. Autofix’s main value proposition, he explained, is that it can help developers speed up the process of triaging and resolving errors in production because it knows about the context the code is running in. “We’re trying to solve problems in production as fast as possible. We’re not trying to make you a faster developer when you’re building your application,” he said.
Using an agent-based architecture, Autofix will keep an eye out for errors and then use its discovery agent to see if a code change could fix that error — and if not, it will provide a reason. What’s important here is that developers remain in the loop at all times. One nifty feature here, for example, is that they can add some additional context for the AI agents if they already have some idea of what the problem may be. Or they can opt to hit the “gimme fix” button and see what the AI comes up with.
The AI will then go through a few steps to assess the issue and create an action plan to fix it. In the process, Autofix will provide developers with a diff that explains the changes and then, if everything looks good, create a pull request to merge those changes.
Autofix supports all major languages, though Elser acknowledged that the team did most of its testing with JavaScript and Python code. Obviously, it won’t always get things right. There is a reason Sentry likens it to a junior developer, after all. The most straightforward failure case, though, Elser told me, is when the AI simply doesn’t have enough context — maybe because the team hasn’t set up enough instrumentation to gather the necessary data for Autofix to work with, for example.
One thing to note here is that while Sentry is looking at building its own models, it is currently working with third-party models from the likes of OpenAI and Anthropic. That also means that users must opt in to send their data to these third-party services to use Autofix. Elser said that the company plans to revisit this in the future and maybe offer an in-house LLM that is fine-tuned on its data.
App monitoring platform Sentry gets $60 million Series D at $1 billion valuation
Meta's X competitor Threads invites developers to sign up for API access, publishes docs
After opening its developer API to select companies for testing in March, Meta’s Twitter/X competitor Threads is now introducing developer documentation and a sign-up sheet for interested parties ahead of the API’s public launch, planned for June.
The new documentation details the API’s current limitations and its endpoints, among other things, which could help developers get started on their Threads-connected apps and any other projects that integrate with the new social network.
For instance, those who want to track analytics around Threads’ posts can use an Insights API to retrieve things like views, likes, replies, reposts, and quotes. There are also details on how to publish posts and media via the API, retrieve replies, and a series of troubleshooting tips.
The documentation indicates that Threads accounts are limited to 250 API-published posts within a 24-hour period and 1,000 replies — a measure to counteract spam or other excessive use. It also offers the image and video specifications for media uploaded with users’ posts and notes that Threads’ text post character counts have a hard limit of 500 characters — longer than old Twitter’s 280 characters, but far less than the 25,000 characters X offers to paid subscribers or the now 100,000 characters it permits in articles posted directly to its platform.
Whether or not Meta will ultimately favor certain kinds of apps over others remains to be seen.
So far, Threads API beta testers have included social tool makers like Sprinklr, Sprout Social, Social News Desk, Hootsuite, and tech news board Techmeme.
Although Threads has begun its integration with the wider fediverse — the network of interconnected social networking services that includes Mastodon and others — it doesn’t appear that fediverse sharing can be enabled or disabled through the API itself. Instead, users still have to visit their settings in the Threads app to publish to the fediverse.
Meta says the new documentation will be updated over time as it gathers feedback from developers. In addition, anyone interested in building with the new API and providing feedback can now request access via a sign-up page — something that could also help Meta track the apps that are preparing to go live alongside the API’s public launch.
Apex Legends hacker says game developers patched exploit used on streamers
Last month, a hacker wreaked havoc during an esports tournament of the popular shooter game Apex Legends, hacking two well-known streamers mid-game to make it look like they were using cheats.
A month later, it seems like the hacking saga may have come to a close with the game developers patching the bug exploited by the hacker.
Because of the hack, the organizers had to suspend the tournament on March 17. Two days later, Apex Legends developer Respawn said on its official X account that it had “deployed the first of a layered series of updates to protect the Apex Legends player community.” Then a week later, the company wrote that it had “added another update that is intended to further protect our players and ensure the competitive integrity of Apex Legends.”
Respawn’s posts don’t clearly say that the updates patched the bugs exploited during the tournament. But the hacker behind the cheating scandal told TechCrunch this week that Respawn’s patches fixed the vulnerability that he had exploited to hack the two streamers.
“The exploit I’ve used in [Apex Legends Global Series] is fully patched,” the hacker, who goes by Destroyer2009, said in an online chat.
Destroyer2009, who previously told TechCrunch that he had hacked the two streamers “for fun,” said he didn’t want to reveal any technical details of the bug he exploited, even if it is now patched.
“No one likes when severe vulnerabilities in your product are exposed publicly. I asked my friend and we both agreed that we don’t really want to publicly expose what happened from a technical perspective yet,” the hacker said, referring to a friend he worked with to develop the hack.
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Referring to an unrelated botched in-game update by Respawn this week, Destroyer2009 said: “[I] don’t think embarrassing them even more is fair.”
Destroyer2009 said he tested his exploit after Respawn’s announcement of the second update on March 26, although he said it’s possible it was patched sooner because he didn’t have a chance to test it before.
Destroyer2009’s hacks were high-profile, disruptive and caused a big stir in the Apex Legends community. The two streamers targeted, ImperialHal and Genburten, collectively have 2.5 million followers on the game-streaming platform Twitch, and several other Apex Legends players and streamers commented on the news of the hacks on their channels.
Yet, Respawn isn’t being forthcoming about the patches it released. TechCrunch asked Respawn and Electronic Arts, the owners of the development studio, to confirm whether the exploit used by Destroyer2009 is indeed patched, and if so, when it was patched.
But neither Respawn nor Electronic Arts responded to TechCrunch’s multiple requests for comment. The two companies did not respond to requests for comment in the last few weeks either.
Meanwhile, Destroyer2009 said he won’t do any more public hacks for now, because “anything more severe than the [Apex tournament hack] accident will be already considered as a real hacking with all the consequences so [probably] will just play the game until it gets boring as usual.”
HoundDog.ai helps developers prevent personal information from leaking
HoundDog.ai, a startup that helps developers ensure their code doesn’t leak personally identifiable information (PII), came out of stealth Wednesday and announced a $3.1 million seed round lead by E14, Mozilla Ventures and ex/ante, in addition to a number of angel investors. Unlike other scanning tools, HoundDog actually looks at the code a developer is writing, using both traditional pattern matching and large language models (LLMs) to find potential issues.
HoundDog was founded by Amjad Afanah, who previously co-founded DCHQ, which was later acquired by Gridstore (which, to complicate things, then changed its name to HyperGrid) in 2016. Afanah also co-founded apisec.ai, which is still up and running, and worked at self-driving startup Cruise. The inspiration for HoundDog came during his time at data security startup Cyral and talking to privacy teams there, he told me.
“When I was at Cyral, we had a lot of data,” he said. “What Cyral does — like many others in the data security space — is they focus on production systems. They help you discover, classify your structured data and your databases, and then help you apply access controls. But the overwhelming feedback that I kept hearing from security and privacy teams alike was: ‘You know, it’s a little too reactive and it doesn’t keep up with the changes in the code base.’”
So HoundDog shifts this process even further left. While it still sits in the continuous integration flow and not yet in the development environment (though that may happen in the future), the idea here is to find potential data leaks before the code is merged. And most importantly, HoundDog does so by looking at the actual code, not the data flow it produces. “Our source of truth is the code base,” Afanah said.
Thanks to this, if a development team starts collecting Social Security numbers, for example, HoundDog would raise a flag and warn the team about that before the code is ever merged; it would also alert the security team. That could potentially be a major — and costly issue — after all.
The service currently supports code written in Java, C#, JavaScript and TypeScript, as well as SQL, GraphQL and OpenAPI/Swagger queries. Support for Python is imminent, the company says.
Afanah noted that a tool like this is becoming especially important in this age of AI-generated code, something Replit CEO (and HoundDog angel investor) Amjad Masad also echoed.
“As an increasing number of companies turn to AI-generated code to accelerate development, embedding security best practices and ensuring the security of the generated code becomes essential,” Masad said. “HoundDog.ai is leading the way in securing PII data early in the development cycle, making it an indispensable component of any AI code generation workflow. This is the reason I chose to invest in this company.”
HoundDog itself does use AI, though, too. It currently relies on OpenAI’s models to do so, but it’s important to stress that this is optional. Users who worry about their code leaving their private repositories can also choose to only rely on the company’s more traditional code scanner.
A major part of HoundDog’s value proposition is that it can cut compliance costs for startups thanks to its automated reporting capabilities. The service can automatically generate a record of processing activities (RoPA). To do this, HoundDog uses generative AI to generate these reports and sends that data to OpenAI. The team does stress that only the tokens the service has discovered through its regular scanner are shared with OpenAI and that the actual source code isn’t shared.
The company offers a limited free plan, with paid plans starting at $200/month for scanning up to two repos.
Unify helps developers find the best LLM for the job
When developers have a particular job that AI can solve, it’s not typically as simple as just pointing an LLM at the data. There are other considerations such as cost, speed and accuracy and finding ways to balance all of those has been particularly challenging, especially with so many new models coming online all the time.
That’s where Unify comes in, a British startup from an Imperial College alum, who has come up with a router tool that lets developers enter parameters and find the best LLM for their unique requirements, whatever they may be. On Wednesday, the company announced an $8 million investment.
“The main objective with Unify is figuring out which models from which providers are best for your task using objective benchmarks and dashboards that let you compare them,” company founder and CEO Daniel Lenton told TechCrunch.
“The router effectively is kind of a natural extension of this process, particularly as companies start to deploy at scale, and the speed and the cost become more important. So what we’re really trying to do is give people much more control over the quality, cost and speed profile of their LLM applications,” Lenton said.
Unsurprisingly, Unify uses AI to run the core router application. “Our router itself is a learned neural network. So it learns which models are best for doing which tasks,” he said. The company does this by running exhaustive benchmarks on each new model on all these tasks using GPT Pro as a judge. From this, the system learns how good this model was doing certain tasks across their training sets.
“So very quickly any new model provider is supported by the router basically a day or two later,” he said.
Lenton says the router, and how they’ve built a unique model to train it, is in itself a way to defend what his startup is doing from encroachment by the bigger players, that and the fact that they are neutral, and the hyperscalers might not be.
He said typically customers are just experimenting with different models and don’t have a tool to track which one is best.
“There are people that have kind of hair on fire problems that are willing to try an existing solution. So I think that’s how we’ve managed to get our foot in the door,” he said.
While there are competitors out there like Martian Router, OpenRouter and Portkey, Lenton says his company is the only one optimizing jointly for quality, cost and speed.
The company is small right now with seven employees, and he is keeping it intentionally lean while he works on getting a fully monetized product in the market. The plan is to add three additional employees this year.
He reports about 3,000 signups so far with a few hundred regular users. They expect to make money as they charge companies for building their own custom benchmarks. Each company can get started with the tool with a $50 credit.
The $8 million investment came from a slew of investors including SignalFire, M12, J12, Essence VC, A. Capital, Lunar VC, Y Combinator and a bunch of prominent industry angels.
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