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Artificial intelligence is the greatest investment opportunity of our lifetime. The time to invest in groundbreaking AI is now, and this stock is a steal!

AI is eating the world—and the machines behind it are ravenous.

Each ChatGPT query, each model update, each robotic breakthrough consumes massive amounts of energy. In fact, AI is already pushing global power grids to the brink.

Wall Street is pouring hundreds of billions into artificial intelligence—training smarter chatbots, automating industries, and building the digital future. But there’s one urgent question few are asking:

Where will all of that energy come from?

AI is the most electricity-hungry technology ever invented. Each data center powering large language models like ChatGPT consumes as much energy as a small city. And it’s about to get worse.

Even Sam Altman, the founder of OpenAI, issued a stark warning:

“The future of AI depends on an energy breakthrough.”

Elon Musk was even more blunt:

“AI will run out of electricity by next year.”

As the world chases faster, smarter machines, a hidden crisis is emerging behind the scenes. Power grids are strained. Electricity prices are rising. Utilities are scrambling to expand capacity.

And that’s where the real opportunity lies…

One little-known company—almost entirely overlooked by most AI investors—could be the ultimate backdoor play. It’s not a chipmaker. It’s not a cloud platform. But it might be the most important AI stock in the US owns critical energy infrastructure assets positioned to feed the coming AI energy spike.

As demand from AI data centers explodes, this company is gearing up to profit from the most valuable commodity in the digital age: electricity.

The “Toll Booth” Operator of the AI Energy Boom

• It owns critical nuclear energy infrastructure assets, positioning it at the heart of America’s next-generation power strategy.
• It’s one of the only global companies capable of executing large-scale, complex EPC (engineering, procurement, and construction) projects across oil, gas, renewable fuels, and industrial infrastructure.
• It plays a pivotal role in U.S. LNG exportation—a sector about to explode under President Trump’s renewed “America First” energy doctrine.

Trump has made it clear: Europe and U.S. allies must buy American LNG.

And our company sits in the toll booth—collecting fees on every drop exported.

But that’s not all…

As Trump’s proposed tariffs push American manufacturers to bring their operations back home, this company will be first in line to rebuild, retrofit, and reengineer those facilities.

AI. Energy. Tariffs. Onshoring. This One Company Ties It All Together.

While the world is distracted by flashy AI tickers, a few smart investors are quietly scooping up shares of the one company powering it all from behind the scenes.

AI needs energy. Energy needs infrastructure.

And infrastructure needs a builder with experience, scale, and execution.

This company has its finger in every pie—and Wall Street is just starting to notice.

Wall Street is noticing this company also because it is quietly riding all of these tailwinds—without the sky-high valuation.

While most energy and utility firms are buried under mountains of debt and coughing up hefty interest payments just to appease bondholders…

This company is completely debt-free.

In fact, it’s sitting on a war chest of cash—equal to nearly one-third of its entire market cap.

It also owns a huge equity stake in another red-hot AI play, giving investors indirect exposure to multiple AI growth engines without paying a premium.

And here’s what the smart money has started whispering…

The Hedge Fund Secret That’s Starting to Leak Out

This stock is so off-the-radar, so absurdly undervalued, that some of the most secretive hedge fund managers in the world have begun pitching it at closed-door investment summits.

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Why? Because excluding cash and investments, this company is trading at less than 7 times earnings.

And that’s for a business tied to:

• The AI infrastructure supercycle
• The onshoring boom driven by Trump-era tariffs
• A surge in U.S. LNG exports
• And a unique footprint in nuclear energy—the future of clean, reliable power

You simply won’t find another AI and energy stock this cheap… with this much upside.

This isn’t a hype stock. It’s not riding on hope.

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This is your chance to get in before the rockets take off!

Disruption is the New Name of the Game: Let’s face it, complacency breeds stagnation.

AI is the ultimate disruptor, and it’s shaking the foundations of traditional industries.

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Posted on nosqlgooglealerts. Visit nosqlgooglealerts

NoSQL Database Market

The NoSQL Database Market Report by The Business Research Company delivers a detailed market assessment, covering size projections from 2025 to 2034. This report explores crucial market trends, major drivers and market segmentation by [key segment categories].

What Is the NoSQL Database Market Size and Projected Growth Rate?
The NoSQL database market size will grow from $11.6 billion in 2024 to $15.59 billion in 2025 at a compound annual growth rate (CAGR) of 34.4%. The growth in the historic period is attributed to increased data volume, need for scalable solutions, limitations of traditional databases, rise of big data analytics, growth of web and mobile applications, demand for flexible data models, and advancements in cloud computing.

The NoSQL database market size is expected to grow to $50.39 billion by 2029, at a CAGR of 34.1%. Growth is driven by the rise of IoT devices, AI, real-time data processing, distributed systems, e-commerce platforms, data security needs, and database technology advances. Trends include multi-model databases, database automation, machine learning integration, edge computing, data privacy, and databa

Purchase the full report for exclusive industry analysis:
https://www.thebusinessresearchcompany.com/purchaseoptions.aspx?id=19616

What Are the Major Segments in the NoSQL Database Market?
The NoSQL databasemarket covered in this report is segmented –

1) By Type: Key-Value Store, Document Database, Column Based Store, Graph Database
2) By Organization Size: Small And Medium Enterprises, Large Enterprises
3) By Application: Data Storage, Mobile Apps, Web Apps, Data Analytics, Other Applications
4) By Industry Vertical: Banking, Financial Services, And Insurance (BFSI), Retail And E- Commerce, Healthcare And Life Sciences, Government And Public Sector, Telecom And Information Technology (IT), Manufacturing

Subsegments:
1) By Key-Value Store: Distributed Key-Value Stores, In-Memory Key-Value Stores, Persistent Key-Value Stores, Caching Solutions
2) By Document Database: Schema-Free Document Databases, Self-Describing Document Databases, Multi-Model Document Databases, Search-Optimized Document Databases
3) By Column-Based Store: Wide-Column Stores, Time-Series Column Stores, Column Family Stores, Distributed Column Stores
4) By Graph Database: Property Graph Databases, RDF Graph Databases, Multi-Model Graph Databases, Graph Analytics Platforms

Get your free sample here:
https://www.thebusinessresearchcompany.com/sample.aspx?id=19616&type=smp

What Are The Driving NoSQL Database Market Evolution?
The increased demand for online gaming and multimedia consumption is expected to boost the growth of the NoSQL database market. The rise in online gaming and digital entertainment is driven by technological advancements and widespread access to high-speed internet. NoSQL databases are crucial for managing large amounts of unstructured data, supporting high-performance queries, and enabling real-time interactions, enhancing the user experience. For example, a study by the Office of Communications reported that individuals in the UK spent an average of seven and a half hours per week on online gaming. Thus, the surge in online gaming and multimedia consumption is propelling the NoSQL database market.

Which Firms Dominate The NoSQL Database Market Segments?
Major companies operating in the NoSQL database market are Google LLC, Microsoft Corporation, Amazon Web Services Inc., International Business Machines Corporation, Oracle Corporation, SAP SE, Hewlett Packard Enterprise (HPE), Databricks Inc., MongoDB Inc., Elastic NV, The Apache Software Foundation, Redis Labs Ltd., Neo4j Inc., DataStax Inc., Couchbase Inc., InfluxData Inc., Aerospike Inc., MapR Technologies Inc., TigerGraph Inc., InfiniteGraph Inc., Basho Technologies Inc., VoltDB Inc., OrientDB Inc., Fauna Inc., NuoDB Inc., RavenDB Ltd.

What Trends Are Driving Growth in The NoSQL Database Market?
Major Companies in the NoSQL database market are forming strategic partnerships to improve technology integration and expand market presence. A strategic partnership typically involves collaboration between two or more Major Companies to combine resources, expertise, and efforts towards common goals. For example, Taashee Linux Services Private Limited (TLSPL), an India-based technology firm, partnered with RavenDB, an Israel-based NoSQL document database company, in December 2022. This partnership enables Taashee’s clients to access RavenDB’s advanced features, such as high-performance ACID-compliant transactions, auto-indexing, and full-text search capabilities, enhancing their ability to provide tailored, scalable database solutions.

Get the full report for exclusive industry analysis:
https://www.thebusinessresearchcompany.com/report/nosql-database-global-market-report

Which Is The Largest Region In The NoSQL Database Market?
North America was the largest region in the NoSQL database market in 2024. The regions covered in the NoSQL database market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

Frequently Asked Questions:
1. What Is the Market Size and Growth Rate of the NoSQL Database Market?
2. What is the CAGR expected in the NoSQL Database Market?
3. What Are the Key Innovations Transforming the NoSQL Database Industry?
4. Which Region Is Leading the NoSQL Database Market?

Why This Report Matters:

Competitive overview: This report analyzes the competitive landscape of the 3D imaging software market, evaluating key players on market share, revenue, and growth factors.

Informed Decisions: Understand key strategies related to products, segmentation, and industry trends.

Efficient Research: Quickly identify market growth, leading players, and major segments.

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Learn More About The Business Research Company
With over 15,000+ reports from 27 industries covering 60+ geographies, The Business Research Company has built a reputation for offering comprehensive, data-rich research and insights. Our flagship product, the Global Market Model delivers comprehensive and updated forecasts to support informed decision-making.

This release was published on openPR.

Posted on mongodb google news. Visit mongodb google news

MongoDB (NASDAQ:MDB) a database management company, is scheduled to report its earnings on Wednesday, June 4, 2025. Historically, MDB stock has reacted negatively to earnings announcements, with a one-day decline in 55% of instances over the past five years. The median drop has been 10%, with a maximum single-day loss of 26.9%.

For event-driven traders, understanding these historical patterns can be advantageous. There are two primary strategies: either position yourself before the earnings release based on historical odds, or analyze the correlation between immediate and medium-term returns after the release to inform your subsequent moves.

Consensus estimates project earnings of $0.66 per share on revenues of $527.5 million. This compares to $0.51 per share on sales of $450.6 million in the year-ago quarter. Fundamentally, MongoDB has a current market capitalization of $15 billion, with $2.0 billion in revenue over the last twelve months. However, the company reported an operating loss of $216 million and a net loss of $129 million during the same period. That said, if you seek upside with lower volatility than individual stocks, the Trefis High Quality portfolio presents an alternative – having outperformed the S&P 500 and generated returns exceeding 91% since its inception. Separately, see – Merck Stock’s Ticking Keytruda Time Bomb.

See earnings reaction history of all stocks

MDB Stock Historical Odds Of Positive Post-Earnings Return

Some observations on one-day (1D) post-earnings returns:

• There are 20 earnings data points recorded over the last five years, with 9 positive and 11 negative one-day (1D) returns observed. In summary, positive 1D returns were seen about 45% of the time.
• However, this percentage decreases to 36% if we consider data for the last 3 years instead of 5.
• Median of the 9 positive returns = 19%, and median of the 11 negative returns = -10%

Additional data for observed 5-Day (5D), and 21-Day (21D) returns post earnings are summarized along with the statistics in the table below.

MDB Stock Correlation Between 1D, 5D, and 21D Historical Returns

A relatively less risky strategy (though not useful if the correlation is low) is to understand the correlation between short-term and medium-term returns post earnings, find a pair that has the highest correlation, and execute the appropriate trade. For example, if 1D and 5D show the highest correlation, a trader can position themselves “long” for the next 5 days if 1D post-earnings return is positive. Here is some correlation data based on 5-year and 3-year (more recent) history. Note that the correlation 1D_5D refers to the correlation between 1D post-earnings returns and subsequent 5D returns.

Learn more about Trefis RV strategy that has outperformed its all-cap stocks benchmark (combination of all 3, the S&P 500, S&P mid-cap, and Russell 2000), to produce strong returns for investors. Separately, if you want upside with a smoother ride than an individual stock like MongoDB, consider the High Quality portfolio, which has outperformed the S&P, and clocked >91% returns since inception.

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

Posted on mongodb google news. Visit mongodb google news

Bourgeon Capital Management LLC purchased a new position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) during the 1st quarter, according to its most recent Form 13F filing with the Securities and Exchange Commission (SEC). The firm purchased 40,060 shares of the company’s stock, valued at approximately $7,027,000. MongoDB makes up about 1.5% of Bourgeon Capital Management LLC’s portfolio, making the stock its 29th biggest holding.

A number of other hedge funds have also recently made changes to their positions in MDB. Strategic Investment Solutions Inc. IL bought a new position in MongoDB during the 4th quarter worth approximately $29,000. NCP Inc. purchased a new stake in shares of MongoDB during the fourth quarter valued at approximately $35,000. Coppell Advisory Solutions LLC grew its holdings in shares of MongoDB by 364.0% in the fourth quarter. Coppell Advisory Solutions LLC now owns 232 shares of the company’s stock worth $54,000 after purchasing an additional 182 shares during the last quarter. Smartleaf Asset Management LLC increased its stake in shares of MongoDB by 56.8% in the fourth quarter. Smartleaf Asset Management LLC now owns 370 shares of the company’s stock worth $87,000 after buying an additional 134 shares during the period. Finally, Manchester Capital Management LLC raised its holdings in MongoDB by 57.4% during the 4th quarter. Manchester Capital Management LLC now owns 384 shares of the company’s stock valued at $89,000 after buying an additional 140 shares during the last quarter. 89.29% of the stock is currently owned by institutional investors.

Insider Activity at MongoDB

In related news, insider Cedric Pech sold 1,690 shares of the firm’s stock in a transaction that occurred on Wednesday, April 2nd. The shares were sold at an average price of $173.26, for a total value of $292,809.40. Following the sale, the insider now owns 57,634 shares in the company, valued at approximately $9,985,666.84. This trade represents a 2.85% decrease in their ownership of the stock. The sale was disclosed in a document filed with the SEC, which can be accessed through the SEC website. Also, Director Hope F. Cochran sold 1,175 shares of the stock in a transaction on Tuesday, April 1st. The shares were sold at an average price of $174.69, for a total value of $205,260.75. Following the transaction, the director now directly owns 19,333 shares in the company, valued at $3,377,281.77. The trade was a 5.73% decrease in their ownership of the stock. The disclosure for this sale can be found here. In the last three months, insiders have sold 22,203 shares of company stock valued at $3,848,569. 3.60% of the stock is owned by corporate insiders.

Analysts Set New Price Targets

A number of brokerages have issued reports on MDB. Barclays cut their price target on shares of MongoDB from $280.00 to $252.00 and set an “overweight” rating on the stock in a report on Friday, May 16th. Wells Fargo & Company cut MongoDB from an “overweight” rating to an “equal weight” rating and cut their target price for the stock from $365.00 to $225.00 in a research note on Thursday, March 6th. Cantor Fitzgerald initiated coverage on MongoDB in a research report on Wednesday, March 5th. They set an “overweight” rating and a $344.00 price target for the company. Canaccord Genuity Group dropped their price objective on MongoDB from $385.00 to $320.00 and set a “buy” rating for the company in a report on Thursday, March 6th. Finally, Monness Crespi & Hardt raised MongoDB from a “sell” rating to a “neutral” rating in a research note on Monday, March 3rd. Nine investment analysts have rated the stock with a hold rating, twenty-three have assigned a buy rating and one has given a strong buy rating to the stock. According to data from MarketBeat, MongoDB has a consensus rating of “Moderate Buy” and a consensus price target of $286.88.

Check Out Our Latest Report on MDB

MongoDB Price Performance

Shares of NASDAQ:MDB opened at $193.00 on Tuesday. The business has a fifty day moving average price of $174.12 and a 200-day moving average price of $231.29. The company has a market capitalization of $15.67 billion, a PE ratio of -70.44 and a beta of 1.49. MongoDB, Inc. has a 1-year low of $140.78 and a 1-year high of $370.00.

MongoDB (NASDAQ:MDB – Get Free Report) last released its quarterly earnings data on Wednesday, March 5th. The company reported $0.19 earnings per share for the quarter, missing analysts’ consensus estimates of $0.64 by ($0.45). The firm had revenue of $548.40 million for the quarter, compared to the consensus estimate of $519.65 million. MongoDB had a negative net margin of 10.46% and a negative return on equity of 12.22%. During the same quarter in the prior year, the business posted $0.86 earnings per share. On average, sell-side analysts anticipate that MongoDB, Inc. will post -1.78 earnings per share for the current fiscal year.

About MongoDB

(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

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).

This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

Before you consider MongoDB, you’ll want to hear this.

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

Article originally posted on InfoQ. Visit InfoQ

Have I Been Pwned (HIBP)– the popular data breach notification service founded by security researcher Troy Hunt, has launched a major front-end redesign aimed at improving breach visibility and laying the groundwork for future capabilities. In an interview with InfoQ, Hunt highlighted automation, family account enrollment, and improved enterprise workflows as immediate next steps, while calling for stronger breach disclosure norms across the industry.

The update introduces a modernized user experience and several new pages focused on helping both individuals and organizations more easily monitor exposed credentials and domains.

Hunt emphasizes that the update was not merely a technical overhaul but a response to the evolving needs of the user base. He mentions that after interacting with users worldwide, a recurring theme emerged: the desire for HIBP to be more approachable and user-friendly. 

Hunt says the rebuild also lays the groundwork for future capabilities – including support for passkeys and delegated account management that reflect emerging security trends and real-world usage patterns. One example he cites is the ability to enroll family members’ email addresses, aimed at users who act as informal tech support for others.

The version 2.0 update introduces several new front-end features aimed at improving usability and situational awareness. The redesigned Search page offers faster response times and a cleaner layout, while a new Breach page (below) provides a clearer breakdown of breach details.

Source: troyhunt.com

The new dashboard page (below) consolidates a user’s monitored email addresses and domain results into a single view, providing persistent visibility into breach activity without requiring a new search each time.

Source: troyhunt.com

Paying subscribers who manage and monitor multiple domains can view who within their organization has been impacted using the overhauled domain search experience.

Furthermore, Hunt discusses the rationale behind specific, smaller changes, such as the removal of username and phone number search support from the website. He explains that these features were initially introduced for specific incidents but were rarely utilized thereafter. The decision to remove them was based on factors like parsing difficulties, notification limitations, and user confusion.

With growing demand from both individuals and organizations – including those managing large sets of users and domains – automation, visibility, and responsible disclosure are seen as ongoing priorities for the application. InfoQ reached out to Troy Hunt to learn more about the motivations behind the release and where he sees HIBP evolving next.

InfoQ: Do you see HIBP evolving as authentication shifts toward passkeys, federated identity, and AI-driven security tooling?

Troy Hunt: These are all great steps forward, but don’t really change the likelihood of data breaches occurring. They do change the impact of them (i.e. passkeys instead of passwords means no reuse and account takeover due to a leaked password), but it won’t stop all your other PII from being exposed.

InfoQ: Now that the release is live, what capabilities or use cases are you most excited to explore next?

Hunt: This is going to sound really boring, but the next priority is to automate requests that we’re currently manually processing – namely enabling resellers to manage their customers and allowing our Director customers to create quotes without raising tickets for us. This has been a bit of an unfortunate reality with running a service that now has paying customers where we need to help them navigate corporate compliance and procurement regimes, and we want to delegate that work away to automated processes as fast as possible. I’m genuinely excited about now adding features such as the ability to enroll family members’ email addresses for people like me who play tech support for everyone else.

InfoQ: In your view, what areas of breach notification or data protection remain underserved and what innovations might we see in the next few years?

Hunt: This is a massive one for me and I’ve literally been travelling the world talking to governments and law enforcement agencies about this: Disclosure. So often, data breach victims simply aren’t told about their exposure and usually, that’s a completely legal position for the company to take. I wrote about it in depth in September: The Data Breach Disclosure Conundrum.

Posted on mongodb google news. Visit mongodb google news

Mackenzie Financial Corp grew its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 47.8% in the 4th quarter, according to the company in its most recent Form 13F filing with the SEC. The institutional investor owned 5,731 shares of the company’s stock after acquiring an additional 1,854 shares during the quarter. Mackenzie Financial Corp’s holdings in MongoDB were worth $1,334,000 at the end of the most recent reporting period.

Other institutional investors have also recently modified their holdings of the company. HighTower Advisors LLC increased its position in shares of MongoDB by 2.0% in the fourth quarter. HighTower Advisors LLC now owns 18,773 shares of the company’s stock valued at $4,371,000 after buying an additional 372 shares in the last quarter. Jones Financial Companies Lllp lifted its stake in shares of MongoDB by 68.0% in the 4th quarter. Jones Financial Companies Lllp now owns 1,020 shares of the company’s stock valued at $237,000 after purchasing an additional 413 shares during the last quarter. Smartleaf Asset Management LLC lifted its stake in shares of MongoDB by 56.8% in the 4th quarter. Smartleaf Asset Management LLC now owns 370 shares of the company’s stock valued at $87,000 after purchasing an additional 134 shares during the last quarter. Clear Creek Financial Management LLC acquired a new position in shares of MongoDB in the 4th quarter valued at $295,000. Finally, Steward Partners Investment Advisory LLC raised its holdings in shares of MongoDB by 12.9% in the 4th quarter. Steward Partners Investment Advisory LLC now owns 1,168 shares of the company’s stock valued at $272,000 after buying an additional 133 shares during the period. 89.29% of the stock is currently owned by hedge funds and other institutional investors.

Insider Buying and Selling at MongoDB

In other news, insider Cedric Pech sold 1,690 shares of the company’s stock in a transaction dated Wednesday, April 2nd. The stock was sold at an average price of $173.26, for a total transaction of $292,809.40. Following the completion of the transaction, the insider now directly owns 57,634 shares of the company’s stock, valued at $9,985,666.84. This represents a 2.85% decrease in their ownership of the stock. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is available at this hyperlink. Also, CFO Srdjan Tanjga sold 525 shares of the company’s stock in a transaction dated Wednesday, April 2nd. The stock was sold at an average price of $173.26, for a total value of $90,961.50. Following the sale, the chief financial officer now owns 6,406 shares in the company, valued at $1,109,903.56. The trade was a 7.57% decrease in their position. The disclosure for this sale can be found here. Insiders have sold 25,203 shares of company stock valued at $4,660,459 in the last 90 days. 3.60% of the stock is currently owned by insiders.

MongoDB Price Performance

MongoDB stock traded down $2.24 during mid-day trading on Friday, hitting $187.12. The company’s stock had a trading volume of 2,627,437 shares, compared to its average volume of 1,891,587. The firm’s 50 day moving average is $174.13 and its 200 day moving average is $231.89. The company has a market cap of $15.19 billion, a PE ratio of -68.29 and a beta of 1.49. MongoDB, Inc. has a 52-week low of $140.78 and a 52-week high of $370.00.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its quarterly earnings results on Wednesday, March 5th. The company reported $0.19 earnings per share for the quarter, missing analysts’ consensus estimates of $0.64 by ($0.45). The firm had revenue of $548.40 million for the quarter, compared to analysts’ expectations of $519.65 million. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. During the same period last year, the business earned $0.86 earnings per share. As a group, analysts forecast that MongoDB, Inc. will post -1.78 EPS for the current year.

Analysts Set New Price Targets

Several equities research analysts recently issued reports on MDB shares. Morgan Stanley cut their price objective on MongoDB from $315.00 to $235.00 and set an “overweight” rating on the stock in a report on Wednesday, April 16th. Mizuho cut their price target on MongoDB from $250.00 to $190.00 and set a “neutral” rating on the stock in a report on Tuesday, April 15th. Loop Capital downgraded MongoDB from a “buy” rating to a “hold” rating and cut their price target for the stock from $350.00 to $190.00 in a report on Tuesday, May 20th. The Goldman Sachs Group lowered their price objective on MongoDB from $390.00 to $335.00 and set a “buy” rating for the company in a research report on Thursday, March 6th. Finally, Wedbush lowered their price objective on MongoDB from $360.00 to $300.00 and set an “outperform” rating for the company in a research report on Thursday, March 6th. Nine equities research analysts have rated the stock with a hold rating, twenty-three have given a buy rating and one has assigned a strong buy rating to the company. According to data from MarketBeat, the stock presently has a consensus rating of “Moderate Buy” and a consensus target price of $286.88.

View Our Latest Stock Analysis on MongoDB

About MongoDB

(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.

Read More

Before you consider MongoDB, you’ll want to hear this.

MarketBeat keeps track of Wall Street’s top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on… and MongoDB wasn’t on the list.

While MongoDB currently has a Moderate Buy rating among analysts, top-rated analysts believe these five stocks are better buys.

View The Five Stocks Here

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At its Build 2025 conference, Microsoft announced plans to open source over the next few months the code behind the GitHub Copilot Chat extension under the MIT license and refactor core AI capabilities directly into the main VS Code codebase. The move, if completed, may affect the ability of current for-pay AI code editors to compete purely on features.

Microsoft cited several reasons for open-sourcing Copilot Chat. They noted the significant advancements in large language models, which have reduced the need for and value of proprietary prompting strategies. In fact, the company Anthropic regularly releases the system prompts for its Claude models. Keeping prompts secret for long remains a challenging endeavor in the face of community-led transparency efforts. AI prompts can additionally be protected by copyright only under certain restrictive circumstances. The same applies to patenting.

Microsoft’s open-sourcing decision also addresses requests from extension authors who needed tighter integration into VS Code than is currently offered by the public extension APIs. The Copilot Chat extension utilised VSCode’s Proposed APIs, a set of unstable APIs implemented in VS Code but not exposed to the public as stable. Regular extension authors, on the other hand, were not able to publish extensions using the Proposed APIs on the Visual Studio Code Marketplace.

The alternative was, as Cursor, Windsurf, et. al. did, to fork Visual Studio Code. As those forks grew in popularity and raised a significant amount of venture capital, Microsoft started to enforce its extension marketplace rules so that forks like Cursor could no longer fetch Microsoft-licensed extensions (e.g., C/C++ extension). By moving the Copilot Chat extension to an MIT license, with its core features integrated into the VS Code core, Microsoft may severely restrict the ability of forks with limited software development teams to compete purely on features against the broader community of extension authors.

Microsoft also quoted the need for increased transparency regarding data collection and improved community-driven security as driving factors for open-sourcing the extension.

This initiative positions VS Code to evolve beyond supporting AI extensions to becoming an “AI-native editor” by default.

Initial developer reactions on platforms like Reddit and Hacker News have shown general approval for the open-sourcing. Discussions often focus on the potential for integrating local AI models, the impact on the competitive editor landscape, and the potential for community contributions to the core AI features. The move is largely seen as a positive step for transparency and the broader developer tools ecosystem.

Visual Studio Code’s product manager chimed in on Reddit:

(vscode pm here)
We do want to open-source the Github Copilot suggestion functionality as well. The current plan is to move all that functionality to the open-source Copilot Chat extension (as step 2). Timeline – next couple of months.
[…] See the engineering plan here

Chat is compatible [with ollama]!

See https://code.visualstudio.com/docs/copilot/language-models#_bring-your-own-language-model-key

Suggestions are not yet compatible – if you want that, we have a feature request that you can upvote. I do want us to add this https://github.com/microsoft/vscode-copilot-release/issues/7690

Microsoft made a flurry of announcements at its Build 2025 conference regarding future products and improvements on current products. The TypeScript team has announced an experimental native port of the TypeScript compiler (tsc), aimed at providing a 10x improvement in build time, drastically reducing cold editor startup times, and substantially improving memory usage. Microsoft has announced Edit, a new open-source command-line text editor, which will be distributed in the future as part of Windows 11. Edit aims to provide a lightweight, native, modern command-line editing experience similar to Nano and Vim.

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LMEval aims to help AI researchers and developers compare the performance of different large language models. Designed to be accurate, multimodal, and easy to use, LMEval has already been used to evaluate major models in terms of safety and security.

One of the reasons behind LMEval is the fast pace at which new models are being introduced. This makes it essential to evaluate them quickly and reliably to assess their suitability for specific applications, says Google researchers. Among its key features are compatibility with a wide range of LLM providers, incremental benchmark execution for improved efficiency, support for multimodal evaluation—including text, images, and code—and encrypted result storage for enhanced security.

For cross-provider support, it is critical that evaluation benchmarks can be defined once and reused across multiple models, despite differences in their APIs. To this end, LMEval uses LiteLLM, a framework that allows developers to use the OpenAI API format to call a variety of LLM providers, including Bedrock, Hugging Face, Vertex AI, Together AI, Azure, OpenAI, Groq, and others. LiteLLM translates inputs to match each provider’s specific requirements for completion, embedding, and image generation endpoints, and produces a uniform output format.

To improve execution efficiency when new models are released, LMEval runs only the evaluations that are strictly necessary, whether for new models, prompts, or questions. This is made possible by an intelligent evaluation engine that follows an incremental evaluation model.

Written in Python and available on GitHub, LMEval requires you to follow a series of steps to run an evaluation. First, you define your benchmark by specifying the tasks to execute, e.g., detect eye colors in a picture, along with the prompt, the image, and the expected results. Then, you list the models to evaluate and run the benchmark:

benchmark = Benchmark(name='Cat Visual Questions',
                      description='Ask questions about cats picture')

...

scorer = get_scorer(ScorerType.contain_text_insensitive)
task = Task(name='Eyes color', type=TaskType.text_generation, scorer=scorer)
category.add_task(task)

# add questions
source = QuestionSource(name='cookbook')
# cat 1 question - create question then add media image
question = Question(id=0, question='what is the colors of eye?',
                    answer='blue', source=source)
question.add_media('./data/media/cat_blue.jpg')
task.add_question(question)

...

# evaluate benchmark on two models
models = [GeminiModel(), GeminiModel(model_version='gemini-1.5-pro')]

prompt = SingleWordAnswerPrompt()
evaluator = Evaluator(benchmark)
eval_plan = evaluator.plan(models, prompt)  # plan evaluation
completed_benchmark = evaluator.execute()  # run evaluation

Optionally, you can save the evaluation results to a SQLite database and export the data to pandas for further analysis and visualization. LMEval uses encryption to store benchmark data and evaluation results to protect against crawling or indexing.

LMEval also includes LMEvalboard, a visual dashboard that lets you view overall performance, analyze individual models, or compare multiple models.

As mentioned, LMEval has been used to create the Phare LLM Benchmark, designed to evaluate LLM safety and security, including resistance to hallucination, factual accuracy, bias, and potential harm.

LMEval is not the only cross-provider LLM evaluation framework currently available. Others include Harbor Bench and EleutherAI’s LM Evaluation Harness. Harbor Bench, limited to text prompts, has the interesting feature of using an LLM to judge result quality. In contrast, EleutherAI’s LM Evaluation Harness includes over 60 benchmarks and allows users to define new ones using YAML.

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The OpenSearch Software Foundation has announced the general availability of OpenSearch 3.0, the first major release in three years and the first since the project joined the Linux Foundation. This version introduces native support for the Model Context Protocol (MCP), along with pull-based data ingestion and gRPC support, aimed at improving scalability and integration.

OpenSearch was launched in 2021 by AWS as a fork of Elasticsearch 7.10, following Elastic’s license change. With performance as a key focus of this release, OpenSearch 3.0 delivers up to 9.5x faster vector search compared to version 1.3, thanks to support for GPU acceleration and more efficient indexing.

OpenSearch 3.0 upgrades to Apache Lucene 10 and introduces enhancements to data ingestion, transport, and management. James McIntyre, senior product marketing manager at AWS, Saurabh Singh, engineering leader at AWS, and Jiaxiang (Peter) Zhu, senior system development engineer at AWS, explain:

The latest version of Apache Lucene offers significant improvements in performance, efficiency, and vector search functionality. These types of improvements pave the way for larger vector and search deployments, enabling AI workloads to scale factorially over time.

Lucene 10 introduces improvements in both I/O and search parallelism, and requires JVM version 21 or later—resulting in some breaking changes and prompting a major version update. Elasticsearch, which reverted to an open source model under the AGPL license last year, recently released version 9.0.0-rc1, which also supports the latest version of Lucene.

The latest OpenSearch release also adds support for gRPC and pull-based ingestion, and introduces reader-writer separation. This allows indexing and search workloads to be configured independently, ensuring consistent, high-performance operation for each. McIntyre, Singh, and Zhu add:

Benefiting from underlying HTTP/2 infrastructure, gRPC supports multiplexing and bidirectional data streams, enabling clients to send and receive requests concurrently over the same TCP connection. Performance gains can be especially pronounced for users working with large and complex queries, where the overhead of deserializing requests can compound when using JSON.

OpenSearch now also supports index type detection and integrates the dynamic data management framework Apache Calcite, enabling iterative query building and exploration. This is achieved by incorporating the query builder into OpenSearch SQL and PPL. In a popular thread on Hacker News, Joe Johnston writes:

Elastic still has the edge on features. Especially Kibana has a lot more features than Amazon’s fork (…) A lot of my consulting clients seem to prefer Opensearch lately. That’s mainly because of the less complicated licensing and the AWS support.

Comparing OpenSearch and Elasticsearch, user Macha adds:

One thing that Opensearch misses that would have been very nice to have on a recent project is enrich processors.

OpenSearch is open source under the Apache 2.0 license. More details about the latest release are available in the release notes on GitHub.