Month: February 2025

Article originally posted on InfoQ. Visit InfoQ

GitHub recently announced the public preview of Linux arm64 hosted runners for GitHub Actions. Free for public repositories, this update provides developers with more efficient tools for building and testing software on Arm-based architectures.

A changelog post on GitHub Blog summarised the announcement. Arm64 runners are hosted environments that enable developers to execute workflows, eliminating the need for cross-compilation or emulation. These 4 vCPU runners, using Cobalt 100 processors, can provide up to a 40% CPU performance increase compared to the previous generation of Microsoft Azure’s Arm-based virtual machines.

The addition of arm64 runners aligns with the increasing demand for arm-based computing, driven by the energy efficiency and performance advantages of the architecture.

Native arm64 execution provides benefits such as faster build times and more reliable testing outcomes compared to emulated environments. At the time of arm64 release on GitHub Actions in June 2024, GitHub had supported Ubuntu and Windows VM images for these runners, enabling a straightforward start for users building on Arm. However, back in June, these runners were available to GitHub Team and Enterprise Cloud Plans customers only.

To use the arm64 hosted runners, include these labels in your workflow files within public repositories: ubuntu-24.04-arm and ubuntu-22.04-arm. These labels are only functional in public repositories; workflows in private repositories using these labels will fail.

Standard runners usage limits, including maximum concurrency based on your plan, apply to all runs in public repositories. Developers are advised to expect potentially longer queue times during peak hours while the arm64 runners are in public preview.

The tech community on Hacker News welcomed this development as we saw interesting discussion threads. One user highlighted how this feature could encourage a broader shift toward ARM-based cloud workflows, mentioning the cost-effectiveness of arm CPUs compared to x64.

Another thread enquired about pricing differences between arm64 and x64 instances. One HN user agartner also provided an example of how to use the native GitHub Actions arm runners to accelerate docker builds.

This capability is particularly beneficial for projects targeting arm devices, such as IoT applications, mobile platforms, and cloud-native services. GitHub has encouraged users to share their experiences and suggestions by joining the community discussion.

For further details, interested readers can visit the documentation and also view a list of VM images from GitHub partners.

Article originally posted on InfoQ. Visit InfoQ

This week’s Java roundup for February 17th, 2025, features news highlighting: the release of Apache NetBeans 25; the February 2025 release of the Payara Platform; the second beta release of Hibernate Reactive 3.0; and the second release candidate of Gradle 8.13.

JDK 24

Build 36 remains the current build in the JDK 24 early-access builds. Further details may be found in the release notes.

JDK 25

Build 11 of the JDK 25 early-access builds was also made available this past week featuring updates from Build 10 that include fixes for various issues. More details on this release may be found in the release notes.

For JDK 24 and JDK 25, developers are encouraged to report bugs via the Java Bug Database.

Spring Framework

It was a busy week over at Spring as the various teams have delivered milestone releases of Spring Boot, Spring Security, Spring Authorization Server, Spring Integration, Spring AI and Spring AMQP. There were also point releases of Spring Framework, Spring for GraphQL,Spring Session, Spring for Apache Kafka and Spring for Apache Pulsar. Further details may be found in this InfoQ news story.

Payara

Payara has released their February 2025 edition of the Payara Platform that includes Community Edition 6.2025.2, Enterprise Edition 6.23.0 and Enterprise Edition 5.72.0. All three releases provide critical bug fixes, component upgrades and a new feature that ensures Docker images shutdown gracefully to allow applications to cleanly terminate without data loss or corruption.

A notable critical issue was an IllegalStateException due to Spring Boot 3 applications failing to deploy to Payara Server 6. This was resolved by ensuring proper initialization of Contexts and Dependency Injection (CDI) during deployment. More details on these releases may be found in the release notes for Community Edition 6.2025.2 and Enterprise Edition 6.23.0 and Enterprise Edition 5.72.0.

Apache Software Foundation

The release of Apache NetBeans 25 delivers many improvements that include: enhancements in support for Java code completion for sealed types in switch statements; improved behaviour with the CloneableEditorSupport class such that it will no longer break additional instances of the Java DocumentFilter class which may be attached to an instance of the Java AbstractDocument class. Further details on this release may be found in the release notes.

The release of Apache Tomcat 9.0.100 provides a resolution to a regression with the release of Tomcat 9.0.99 that caused an error while starting Tomcat on JDK 17. The regression was a mitigation for CVE-2024-56337, a Time-of-Check-Time-of-Use vulnerability in which a write-enabled default servlet for a case insensitive file system can bypass Tomcat’s case sensitivity checks and cause an uploaded file to be treated as a JSP leading to a remote code execution. More details on this release may be found in the release notes.

Hibernate

The second beta release of Hibernate Reactive 3.0.0 ships with resolutions to notable issue such as: a ClassCastException from an instance of the ReactiveEmbeddableForeignKeyResultImpl class due to use of the Hibernate ORM EmbeddableInitializerImpl class instead of its reactive version, namely the ReactiveEmbeddableInitializerImpl class; and a NullPointerException when retrieving an entity using a Jakarta Persistence @ManyToOne composite table with additional properties in the Jakarta Persistence @IdClass annotation. This release is compatible with Hibernate ORM 7.0.0.Beta4, and an upgrade to Vert.x SQL client 4.5.13. Further details on this release may be found in the changelog.

JobRunr

The release of JobRunr 7.4.1 ships with bug fixes and new features such as: the ability to switch between different date styles in job table views, e.g., the timestamp when an instance of the Job class was enqueued; and an enhanced display for more complex job parameters on the job details page. More details on this release may be found in the release notes.

Gradle

The second release candidate of Gradle 8.13.0 introduces a new auto-provisioning utility that automatically downloads a JVM required by the Gradle Daemon. Other notable enhancements include: an explicit Scala version configuration for the Scala Plugin to automatically resolve required Scala toolchain dependencies; and refined millisecond precision in JUnit XML test event timestamps. Further details on this release may be found in the release notes.

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Two critical flaws in the open-source Mongoose Object Data Modeling (ODM) library for MongoDB and Node.js, along with proof-of-concept (PoC) exploits for both vulnerabilities, were detailed in a blog post by OPSWAT on Thursday.  

The flaws are tracked as CVE-2024-53900 and CVE-2025-23061 and have critical CVSS 3 scores of 9.1 and 9.0, respectively.

CVE-2024-53900, which was first discovered and patched in November 2024, can lead to remote code execution (RCE) on the Node.js application server via search injection. CVE-2025-23061, fixed last month, is a bypass of the original patch for CVE-2024-53900, and can lead to RCE by slightly altering the exploit code.

Mongoose helps streamline interactions between MongoDB and Node.js applications and is widely used by application developers, having more than 27,000 stars and 3,800 forks on GitHub, and more than 19,000 dependents in the NPM package repository.

Both vulnerabilities involve the $where operator, which can be used with the populate() function to filter which data is retrieved from MongoDB documents to replace references and populate the application. The $where allows the execution of arbitrary JavaScript code to define specific data retrieval criteria, meaning malicious code could be executed if an attacker controls the input after the $where operator.

Attempting to execute malicious code on the MongoDB server using the $where operator would typically result in an error, noted OPSWAT, as execution on the MongoDB server is restricted to a predefined list of basic operations and functions.  

However, OPSWAT Critical Infrastructure Cybersecurity Graduate Fellow Dat Phung, who discovered both vulnerabilities, found that malicious code under the $where operator could be passed to a function within populate() known as sift(), which would lead the arbitrary code to be executed locally on the application server.

By crafting a query to ensure the request will be passed to sift(), while avoiding triggering the MongoDB server error by including a variable MongoDB does not recognize, Phung was able to achieve RCE on a Node.js application server.  

The Mongoose maintainers fixed the CVE-2024-53900 vulnerability in version 8.8.3, by disallowing the use of $where within the match property passed to populate(). However, Phung found that the $where operator could still be passed to populate() if it was nested within an $or operator, constituting the flaw tracked as CVE-2025-23061.

Phung developed a new PoC exploit showing that RCE could still be achieved on an application server by nesting $where inside an $or clause, despite the initial patch. This bypass flaw was fixed in Mongoose version 8.9.5, and OPSWAT recommends developers upgrade to the latest Mongoose versions to resolve both flaws.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Two critical-severity vulnerabilities in the Mongoose Object Data Modeling (ODM) library for MongoDB could have allowed attackers to achieve remote code execution (RCE) on the Node.js application server, cybersecurity platform OPSWAT reports.

Widely adopted in production environments, Mongoose enables the mapping of JavaScript objects to MongoDB documents, leading to easier data management and validation. However, the function that improves working with relationships between documents could be exploited for RCE.

The first of the critical-severity flaws in the library, tracked as CVE-2024-53900, could allow an attacker to exploit the $where value to potentially achieve RCE on Node.js. The second issue, tracked as CVE-2025-23061, is a bypass for CVE-2024-53900’s patch.

As OPSWAT explains, $where is a MongoDB query operator that enables the execution of JavaScript directly on the MongoDB server, but with certain limitations.

When processing retrieved data, one of Mongoose’s functions would pass the $where value to a function imported from an external library, which would process the queries locally on the application server, without performing input validation.

“This lack of input validation and restriction introduces a significant security vulnerability, as the ‘params’ value- directly controlled by user input – can be exploited, potentially leading to code injection attacks,” OPSWAT notes.

The patch for CVE-2024-53900 added a check to disallow passing the $where operator to the vulnerable function, thus preventing the execution of malicious payloads.

However, the patch could be bypassed by embedding the $where operator in the $or operator supported by both MongoDB and the vulnerable function.

“As a result, an attacker can nest $where under $or to evade the patch’s single-level check. Because Mongoose inspects only the top-level properties of each object in the match array, the bypass payload remains undetected and eventually reaches the sift library, enabling the malicious RCE,” OPSWAT notes.

The cybersecurity organization has released proof-of-concept (PoC) exploit code targeting both vulnerabilities and recommends updating Mongoose to version 8.9.5 or later, which contain complete patches for the two bugs.

Related: Atlassian Patches Critical Vulnerabilities in Confluence, Crowd

Related: OpenSSH Patches Vulnerabilities Allowing MitM, DoS Attacks

Related: Chrome 133, Firefox 135 Updates Patch High-Severity Vulnerabilities

Related: Critical Vulnerability Patched in Juniper Session Smart Router

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Maytree Asset Management Ltd purchased a new position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) in the fourth quarter, according to its most recent filing with the Securities and Exchange Commission. The institutional investor purchased 4,640 shares of the company’s stock, valued at approximately $1,080,000. MongoDB accounts for 2.4% of Maytree Asset Management Ltd’s investment portfolio, making the stock its 5th biggest holding.

A number of other hedge funds and other institutional investors have also recently modified their holdings of the business. Private Advisor Group LLC raised its position in MongoDB by 21.1% in the fourth quarter. Private Advisor Group LLC now owns 9,256 shares of the company’s stock valued at $2,155,000 after purchasing an additional 1,613 shares during the period. Alberta Investment Management Corp purchased a new stake in shares of MongoDB in the 4th quarter valued at $5,895,000. Global Retirement Partners LLC lifted its stake in MongoDB by 1,591.3% during the 4th quarter. Global Retirement Partners LLC now owns 1,945 shares of the company’s stock worth $453,000 after acquiring an additional 1,830 shares in the last quarter. Kornitzer Capital Management Inc. KS purchased a new position in MongoDB during the 4th quarter valued at about $285,000. Finally, Venturi Wealth Management LLC grew its position in MongoDB by 20.6% in the 4th quarter. Venturi Wealth Management LLC now owns 755 shares of the company’s stock valued at $176,000 after acquiring an additional 129 shares in the last quarter. 89.29% of the stock is owned by institutional investors and hedge funds.

MongoDB Price Performance

NASDAQ MDB traded down $16.74 during mid-day trading on Friday, hitting $273.26. The company’s stock had a trading volume of 1,580,897 shares, compared to its average volume of 1,505,249. MongoDB, Inc. has a 52-week low of $212.74 and a 52-week high of $459.70. The stock’s fifty day simple moving average is $261.59 and its two-hundred day simple moving average is $273.25. The firm has a market capitalization of $20.35 billion, a P/E ratio of -99.73 and a beta of 1.28.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its earnings results on Monday, December 9th. The company reported $1.16 earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of $0.68 by $0.48. The business had revenue of $529.40 million during the quarter, compared to analyst estimates of $497.39 million. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. The company’s revenue for the quarter was up 22.3% compared to the same quarter last year. During the same quarter last year, the firm earned $0.96 earnings per share. On average, equities analysts forecast that MongoDB, Inc. will post -1.78 earnings per share for the current fiscal year.

Wall Street Analysts Forecast Growth

Several analysts have recently issued reports on the stock. Cantor Fitzgerald began coverage on shares of MongoDB in a research report on Friday, January 17th. They set an “overweight” rating and a $344.00 price target on the stock. Robert W. Baird raised their price target on shares of MongoDB from $380.00 to $390.00 and gave the company an “outperform” rating in a research report on Tuesday, December 10th. Mizuho upped their price objective on MongoDB from $275.00 to $320.00 and gave the stock a “neutral” rating in a research report on Tuesday, December 10th. Stifel Nicolaus lifted their target price on MongoDB from $325.00 to $360.00 and gave the company a “buy” rating in a report on Monday, December 9th. Finally, Tigress Financial boosted their target price on MongoDB from $400.00 to $430.00 and gave the stock a “buy” rating in a research note on Wednesday, December 18th. Two investment analysts have rated the stock with a sell rating, four have given a hold rating, twenty-three have given a buy rating and two have issued a strong buy rating to the company’s stock. Based on data from MarketBeat.com, the company has a consensus rating of “Moderate Buy” and an average price target of $361.00.

Get Our Latest Stock Analysis on MongoDB

Insider Activity

In other news, CAO Thomas Bull sold 169 shares of the business’s stock in a transaction dated Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total value of $39,561.21. Following the completion of the sale, the chief accounting officer now owns 14,899 shares of the company’s stock, valued at approximately $3,487,706.91. The trade was a 1.12 % decrease in their position. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which is available at the SEC website. Also, Director Dwight A. Merriman sold 2,000 shares of the company’s stock in a transaction dated Monday, November 25th. The shares were sold at an average price of $349.17, for a total transaction of $698,340.00. Following the completion of the sale, the director now owns 1,124,006 shares in the company, valued at approximately $392,469,175.02. This trade represents a 0.18 % decrease in their position. The disclosure for this sale can be found here. Insiders sold 43,979 shares of company stock worth $11,963,757 over the last ninety days. Corporate insiders own 3.60% of the company’s stock.

MongoDB Company Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Rhumbline Advisers raised its position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 0.5% in the fourth quarter, according to the company in its most recent filing with the SEC. The institutional investor owned 97,138 shares of the company’s stock after purchasing an additional 467 shares during the quarter. Rhumbline Advisers owned 0.13% of MongoDB worth $22,615,000 at the end of the most recent quarter.

Several other hedge funds and other institutional investors have also recently made changes to their positions in MDB. Creative Planning lifted its position in MongoDB by 16.2% during the third quarter. Creative Planning now owns 17,418 shares of the company’s stock valued at $4,709,000 after purchasing an additional 2,427 shares during the period. Bleakley Financial Group LLC increased its stake in shares of MongoDB by 10.5% in the third quarter. Bleakley Financial Group LLC now owns 939 shares of the company’s stock worth $254,000 after purchasing an additional 89 shares in the last quarter. Blue Trust Inc. increased its stake in shares of MongoDB by 72.3% in the third quarter. Blue Trust Inc. now owns 927 shares of the company’s stock worth $232,000 after purchasing an additional 389 shares in the last quarter. Sequoia Financial Advisors LLC purchased a new stake in shares of MongoDB in the third quarter worth $302,000. Finally, Signaturefd LLC grew its stake in MongoDB by 11.7% during the third quarter. Signaturefd LLC now owns 1,752 shares of the company’s stock valued at $474,000 after acquiring an additional 183 shares in the last quarter. Institutional investors own 89.29% of the company’s stock.

Insider Buying and Selling

In other MongoDB news, CFO Michael Lawrence Gordon sold 1,245 shares of the company’s stock in a transaction dated Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total value of $291,442.05. Following the transaction, the chief financial officer now owns 79,062 shares of the company’s stock, valued at $18,507,623.58. This represents a 1.55 % decrease in their ownership of the stock. The transaction was disclosed in a filing with the SEC, which is available through this link. Also, insider Cedric Pech sold 287 shares of the company’s stock in a transaction dated Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total transaction of $67,183.83. Following the completion of the transaction, the insider now directly owns 24,390 shares in the company, valued at $5,709,455.10. The trade was a 1.16 % decrease in their position. The disclosure for this sale can be found here. In the last quarter, insiders sold 43,979 shares of company stock valued at $11,963,757. 3.60% of the stock is owned by corporate insiders.

MongoDB Price Performance

MDB opened at $290.00 on Friday. The firm has a fifty day moving average price of $261.59 and a 200 day moving average price of $273.25. MongoDB, Inc. has a 1 year low of $212.74 and a 1 year high of $459.70. The stock has a market cap of $21.60 billion, a P/E ratio of -105.84 and a beta of 1.28.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings results on Monday, December 9th. The company reported $1.16 EPS for the quarter, topping the consensus estimate of $0.68 by $0.48. The company had revenue of $529.40 million during the quarter, compared to analyst estimates of $497.39 million. MongoDB had a negative net margin of 10.46% and a negative return on equity of 12.22%. The firm’s revenue was up 22.3% compared to the same quarter last year. During the same quarter in the previous year, the firm earned $0.96 EPS. Equities analysts expect that MongoDB, Inc. will post -1.78 EPS for the current year.

Wall Street Analysts Forecast Growth

MDB has been the subject of several research analyst reports. Mizuho raised their target price on shares of MongoDB from $275.00 to $320.00 and gave the company a “neutral” rating in a research note on Tuesday, December 10th. JMP Securities restated a “market outperform” rating and issued a $380.00 target price on shares of MongoDB in a report on Wednesday, December 11th. Loop Capital raised their price target on shares of MongoDB from $315.00 to $400.00 and gave the stock a “buy” rating in a research note on Monday, December 2nd. Wells Fargo & Company raised their price target on shares of MongoDB from $350.00 to $425.00 and gave the stock an “overweight” rating in a research note on Tuesday, December 10th. Finally, Stifel Nicolaus raised their target price on shares of MongoDB from $325.00 to $360.00 and gave the company a “buy” rating in a research report on Monday, December 9th. Two analysts have rated the stock with a sell rating, four have given a hold rating, twenty-three have issued a buy rating and two have assigned a strong buy rating to the stock. According to MarketBeat.com, MongoDB presently has a consensus rating of “Moderate Buy” and an average price target of $361.00.

Check Out Our Latest Analysis on MDB

MongoDB Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Featured Articles

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Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Empowered Funds LLC decreased its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 17.4% in the fourth quarter, according to its most recent 13F filing with the SEC. The institutional investor owned 1,287 shares of the company’s stock after selling 271 shares during the period. Empowered Funds LLC’s holdings in MongoDB were worth $300,000 as of its most recent SEC filing.

Several other institutional investors and hedge funds also recently made changes to their positions in MDB. Hilltop National Bank boosted its position in MongoDB by 47.2% during the fourth quarter. Hilltop National Bank now owns 131 shares of the company’s stock valued at $30,000 after buying an additional 42 shares during the period. Brooklyn Investment Group bought a new stake in shares of MongoDB in the 3rd quarter valued at about $36,000. Continuum Advisory LLC boosted its holdings in MongoDB by 621.1% during the 3rd quarter. Continuum Advisory LLC now owns 137 shares of the company’s stock valued at $40,000 after acquiring an additional 118 shares during the period. Versant Capital Management Inc grew its position in MongoDB by 1,100.0% during the 4th quarter. Versant Capital Management Inc now owns 180 shares of the company’s stock worth $42,000 after acquiring an additional 165 shares during the last quarter. Finally, Wilmington Savings Fund Society FSB purchased a new position in MongoDB in the 3rd quarter worth approximately $44,000. Institutional investors own 89.29% of the company’s stock.

MongoDB Stock Down 1.7 %

MDB stock opened at $290.00 on Friday. The business has a 50 day moving average of $261.59 and a 200 day moving average of $273.25. The firm has a market cap of $21.60 billion, a PE ratio of -105.84 and a beta of 1.28. MongoDB, Inc. has a twelve month low of $212.74 and a twelve month high of $459.70.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings results on Monday, December 9th. The company reported $1.16 earnings per share for the quarter, beating the consensus estimate of $0.68 by $0.48. The firm had revenue of $529.40 million during the quarter, compared to the consensus estimate of $497.39 million. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. The company’s revenue was up 22.3% on a year-over-year basis. During the same period last year, the company earned $0.96 earnings per share. On average, research analysts expect that MongoDB, Inc. will post -1.78 earnings per share for the current fiscal year.

Insider Transactions at MongoDB

In related news, CEO Dev Ittycheria sold 2,581 shares of MongoDB stock in a transaction dated Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total value of $604,186.29. Following the completion of the sale, the chief executive officer now directly owns 217,294 shares in the company, valued at approximately $50,866,352.46. This trade represents a 1.17 % decrease in their position. The transaction was disclosed in a filing with the Securities & Exchange Commission, which can be accessed through the SEC website. Also, insider Cedric Pech sold 287 shares of the business’s stock in a transaction dated Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total value of $67,183.83. Following the completion of the transaction, the insider now directly owns 24,390 shares of the company’s stock, valued at approximately $5,709,455.10. This trade represents a 1.16 % decrease in their position. The disclosure for this sale can be found here. Over the last 90 days, insiders have sold 43,979 shares of company stock worth $11,963,757. Corporate insiders own 3.60% of the company’s stock.

Analyst Upgrades and Downgrades

A number of analysts have recently weighed in on MDB shares. Scotiabank dropped their price objective on MongoDB from $350.00 to $275.00 and set a “sector perform” rating for the company in a research note on Tuesday, January 21st. Loop Capital increased their price target on shares of MongoDB from $315.00 to $400.00 and gave the company a “buy” rating in a research report on Monday, December 2nd. Royal Bank of Canada boosted their price objective on shares of MongoDB from $350.00 to $400.00 and gave the company an “outperform” rating in a research report on Tuesday, December 10th. Oppenheimer upped their price objective on shares of MongoDB from $350.00 to $400.00 and gave the company an “outperform” rating in a research note on Tuesday, December 10th. Finally, Canaccord Genuity Group raised their target price on MongoDB from $325.00 to $385.00 and gave the stock a “buy” rating in a research note on Wednesday, December 11th. Two analysts have rated the stock with a sell rating, four have assigned a hold rating, twenty-three have given a buy rating and two have given a strong buy rating to the company. Based on data from MarketBeat, the stock currently has an average rating of “Moderate Buy” and an average target price of $361.00.

Get Our Latest Report on MongoDB

MongoDB Company Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Featured Stories

Want to see what other hedge funds are holding MDB? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for MongoDB, Inc. (NASDAQ:MDB – Free Report).

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Article originally posted on InfoQ. Visit InfoQ

Software architects and system architects will not be replaced anytime soon by generative artificial intelligence (AI) or large language models (LLMs), Avraham Poupko said. They will be replaced by software architects who know how to leverage generative AI and LLMs, and just as importantly, know how NOT to use generative AI.

Avraham Poupko gave a talk about how he uses artificial intelligence in his daily work as an architect at OOP conference.

The word LLM means Large Language Model. Poupko argued that the question of how humans and machines differ is a fundamental question, and understanding it is critical to the understanding of LLMs.

Humans do not really have a language model, they have a world model, Poupko said. Humans have an understanding of the world that consists of an understanding of how the objects in the world behave, and how they interact with each other. That world model is the result of many experiences and interactions, Poupko explained:

When we use spoken and written language to communicate about the world, that is only one representation of the world model and a very limited representation at that.

LLMs on the other hand only have a word or language model, Poupko mentioned. The LLMs only know how words relate to each other. While that model often does give an astonishing illusion of understanding and comprehension, it is not real understanding. It is just a sequence of words, he said.

While it is true that a great deal of world knowledge can be captured in texts and in words, other parts of our world knowledge and world understanding are based on experience and cannot be properly captured in words, Poupko said. This is particularly true with situations that are highly contextual and where the person present is aware of the context, but where not all the context is verbal, he added.

Architects and large language models can work together to create better software architecture, Poupko said, which is somewhat similar to how humans and books can work together:

Humans will use books to learn and understand. A human will decide if the case at hand is similar to the case outlined in the book, and if so will apply the knowledge learned from the book in a context-appropriate way.

That is exactly what we do with an LLM, Poupko stated. We give it a prompt or series of prompts and receive a response. The response is usually neither correct nor incorrect, rather the response is either useful or not useful (as the famous George Box quote goes “All models are wrong. Some are useful”), he mentioned. When we say useful, we mean useful to humans. It is the human that gets to decide if indeed the model is useful and in what context, and then decides to apply it, he mentioned.

Poupko mentioned that that AI is most useful in tasks that involve written language. A case where he often uses LLMs is when there is a need to read a requirements document and discover ambiguities, i.e. those cases where a single requirement can mean multiple things.

In the talk, he gave an example where an online system had the requirement:

The system should be able to handle a large number of users.

When asked to detect ambiguities, the LLM he was working with, detected two ambiguities:

1. The term “a large number of users” is not useful as it is not specific enough. How many users is a “large number”? 100? 1,000,000?
2. The term “a large number of users” can either mean a database that supports a large number of users that are registered to the system, or it can mean a large number of concurrent users. It might of course mean both.

Next Poupko used the LLM to explore what information was needed in order to resolve these ambiguities.

AI does not do design work for me, Poupko said. The system knowledge, the domain knowledge, and the organizational knowledge needed to do effective architecture are such that AI can’t replace me, he concluded.

Article originally posted on InfoQ. Visit InfoQ

On January 13, 2025, GitHub released a new update to GitHub Issues, entering public preview with a host of new features designed to improve project management for development teams. The focus of the update is on breaking down complexities with sub-issues.

The introduction of sub-issues allows developers to create a parent-child hierarchy for tasks. This feature is handy for breaking down complex projects into smaller, manageable pieces.

Teams can now nest related tasks under a parent issue, allowing for better organization and a clearer representation of work breakdown. By structuring issues hierarchically, teams can group related activities, providing visibility between dependencies and tasks.

Additionally, teams can track progress at a granular level within the hierarchy, enabling them to monitor the status of each sub-task independently. This level of detail provides greater visibility into how smaller components contribute to the overall project. By breaking down work into manageable parts, teams can allocate resources more efficiently and identify potential bottlenecks early in the process.

Moreover, teams can monitor the overall status of sub-issues directly from their projects, making it easier to assess progress without needing to open individual tasks. This centralized view provides real-time insights into which areas require attention. By maintaining a structured issue hierarchy, teams can visualize dependencies, manage workloads, and track execution across multi-layered initiatives.

The new issue types feature introduces a structured approach to categorizing work across repositories. By defining shared types such as “bug,” “task,” or “initiative,” teams can establish a common language for organizing issues, making it easier to track their status across multiple projects. This classification system provides a clearer overview of the distribution of work, from high-level initiatives to unresolved bug fixes, fostering better alignment and offering valuable insights into project priorities.

In addition, advanced search functionality enhances issue tracking by allowing users to create complex queries using AND and OR operators with parentheses. This enables precise filtering, making it easier to find specific issues based on defined criteria, such as searching for all bugs and tasks within a repository. With these capabilities, teams can efficiently navigate their projects, ensuring a more streamlined and organized workflow.

This enhancement addresses the challenge of sifting through large volumes of issues, making it easier to locate and prioritize tasks, according to GitHub.

The update sees GitHub enhancing its project and team management, moving GitHub closer to a full development team hub, similar to its Microsoft counterpart – AzureDevOps. Over the past few years, they’ve introduced boards, roadmap views, and workflow automation features to allow it to become a more comprehensive tool suitable for. These new updates appear designed to operate across different methodologies, such as Scrum or Kanban, and promise deeper integration with CI/CD pipelines and third-party tools.

To enhance usability and performance, GitHub has introduced several updates to the Issues UI. The revamped filter bar now features autocomplete and syntax highlighting, making it easier to refine searches. A new “create more” option streamlines the process of adding multiple issues, while issue forms and templates are now sorted alphabetically for quicker access. Sharing issue URLs is more convenient with the addition of a “copy link” button, and the event loader has been improved to retrieve up to 150 entries at once, reducing the need for excessive clicks when navigating long threads.

Developers and teams can enable the public preview in their repositories and experiment with the features to refine workflows and provide feedback.

Article originally posted on InfoQ. Visit InfoQ

Latin America is advancing in the development of artificial intelligence with the creation of Latam-GPT, a language model designed to better represent the history, culture, and linguistic diversity of the region. Announced at the Paris AI Action Summit, the project is led by Chile’s Ministry of Science, Technology, Knowledge, and Innovation (CTCI) and the National Center for Artificial Intelligence (Cenia), with support from experts and institutions across Latin America.

The primary issue addressed by Latam-GPT is the misinterpretation or generalization of phrases, idioms, and cultural references common in current AI models when applied to the Latin American context. This model has been developed with 50B parameters and a database, comparable to OpenAI’s GPT-3.5. The focus on Latin American content allows for more accurate responses to historical, social, and cultural inquiries specific to the region.

The development of Latam-GPT has been funded through an investment, with contributions from the Andean Development Corporation and the University of Tarapacá. Collaborating countries include Argentina, Colombia, Ecuador, Mexico, Peru, and Uruguay, alongside support from institutions in the United States and Spain.

As an open-source initiative, it enables developers and researchers to tailor applications to regional requirements. Its potential uses span education, public policy analysis, environmental research, and economic studies. The model’s open nature fosters innovation and adaptation to local challenges. Currently in the training phase, it invites public participation by collecting Spanish texts that capture linguistic nuances from different countries. This crowdsourced approach helps refine the model’s understanding of regional dialects and cultural contexts.

Comments have highlighted interest and discussions around Latam-GPT, indicating a blend of optimism about fostering digital sovereignty and skepticism about its practical applications beyond educational and cultural research.

User Daniel Ramirez commented on X:

LatamGPT: The First AI Model Made in Latin America Developed by @cen_ia, trained with local data, and featuring 50B parameters, it aims to reduce biases and improve accuracy in the region. Will it be key to digital sovereignty?

And user quasar_buckhead shared:

Bravo Latin America, Bravo! This is exactly what I have been talking about for the last month! There is no “One AI LLM”. Every AI nation should develop their own LLMs for their own needs and disciplines! Excellent Work Latin America! Bravo!

Latam-GPT represents a significant step toward enhancing technological sovereignty and reducing reliance on external AI solutions in Latin America. By focusing on regional specificity, the model aims to foster more inclusive and equitable technological advancements, reflecting the rich diversity of Latin America’s culture and history. Initiatives like Latam-GPT and OpenEuroLLM highlight regional efforts to develop AI models tailored to local cultural and linguistic contexts. Other similar initiatives include SeaLLMs for Southeast Asia, SEA-LION for Singapore, Mistral Saba for Arabic-speaking countries, and Orange’s African Language Model for regional African languages.