Ivelina Dineva shares startup funding intelligence through EverythingStartups, tracking venture fund announcements and startup funding rounds and highlighting resources and funding opportunities for founders.
Ivelina Dineva is useful for concise startup funding intelligence and founder funding opportunities.
Best for
A B2B startup data platform expanding funding intelligence resources fits best.
Not for
An enterprise cybersecurity vendor selling to security practitioners does not fit.
Key takeaways
AI featured strongly in a roundup of 10 U.S. startup funding rounds.
September 2026 fund post reported 55 funds and $16.8B capital.
NSF Seed Fund post gives phase amounts, eligible technology areas, deadline.
Check first: Confirm sponsorship terms, disclosure requirements, and post-level analytics before committing budget.
Main ideas
AI is absorbing funding and spreading across sectors · Headline totals hide how concentrated the money is · Most new funds are small and focused on seed
Ivelina Dineva shares startup funding intelligence through EverythingStartups.
Their posts track venture funding rounds and new fund announcements, and explain funding opportunities for founders.
Follow them for concise snapshots of where capital is going and practical details on how founders may access it.
Dineva turns funding data into readable comparisons.
In a roundup of 10 U.S. startup rounds, they list each company’s funding, valuation, sector and investors, then highlight how strongly AI featured.
In a separate analysis of 55 new venture funds, they compare fund size, stage, geography and investment focus, showing that Europe led by fund count while the U.S. led in dollars.
They also cover funding beyond venture capital: the NSF Seed Fund post gives phase amounts, eligible technology areas and an application deadline.
Together, these examples show a useful range—from market snapshots to actionable founder information.
Ivelina Dineva publishes funding data and analyzes what it means.
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Across the posts, the through-line is that capital is pooling into AI and a small number of very large funds and rounds, while most new funds are small and seed-focused.
Dineva also argues that founders should treat non-dilutive government funding as part of the same picture as venture capital.
Their short, number-dense posts consistently direct readers to a maintained database.
AI is absorbing funding and spreading across sectors
Dineva treats AI as the main story in both new fund formation and the largest private rounds, arguing that it is no longer a standalone category.
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AI’s significance lies not only in attracting the most money but also in appearing alongside infrastructure, robotics, defense, healthcare and manufacturing—a shift that changes how founders and investors categorize companies.
In the September 2026 fund breakdown, 28 of the 55 tracked funds explicitly named AI in their investment focus.
In the same week’s roundup of the ten largest U.S. rounds, most entries were AI companies or AI infrastructure.
The list included an AI cybersecurity round and a physical AI round, not just model builders.
Dineva states the shift plainly: AI increasingly appears alongside infrastructure, robotics, defense, healthcare and manufacturing rather than in its own category.
Headline totals hide how concentrated the money is
Dineva’s recurring point is that a big aggregate number doesn’t mean the money is widely spread.
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A handful of funds and companies take most of it, so founders should look at the top of the list, not just the total.
Of the $16.8B in newly announced fund capital tracked in September 2026, five funds accounted for 61% of the total, led by Bessemer Venture Partners at $5.75B. In the weekly roundup, four companies alone carried more than $22.7B in combined valuation.
Non-dilutive government funding belongs in the same conversation as venture capital
Dineva presents public funding programs as a practical option for founders, highlighting the no-equity terms as a reason to pay attention.
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The coverage extends beyond venture rounds to funding that doesn’t cost founders ownership.
A post walks through the U.S.
National Science Foundation’s Seed Fund: up to $305K in Phase I, up to $1.25M in Phase II, up to $1.555M through the Fast Track pathway, and roughly $2M with supplements, with no equity taken.
It lists the covered technology areas, explains the Project Pitch step and names November 4, 2026, as the next full proposal deadline.
Make the post useful, then send readers to the full data set
Each post gives readers a concise summary, then points them to the same maintained resources: the post is the entry point, the database the product.
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Those links let the posts stay short while carrying plenty of figures.
The weekly roundup and the NSF post both link to the EverythingStartups venture fund database, described as updated daily with funds, stages, sectors, check sizes and partner contact details. The roundup also links to a list of more than 45 startup accelerators, and the fund post asks readers which company they would bet on.
See how Dineva turns raw funding data into a quick, scannable snapshot of a week in large U.S. startup funding and the concentration of AI capital.
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Dineva lists ten funding rounds from a single week, totaling $2.86B.
Each entry names the company, amount, valuation, sector, and lead investors.
The post notes that AI dominated: Instinct raised $1B at a $10B valuation one year after launch, and four companies account for $22.7B+ in combined valuation.
It also points readers to an EverythingStartups newsletter and a venture fund database.
For brands planning a campaign
This concise, data-backed roundup is a natural fit for a funding intelligence platform to co-produce or sponsor. Dineva turns a week of deal data into a readable list highlighting concentration and sector trends, a strong format for weekly funding recaps.
This post answers what new venture capital was raised in a month and where it is going.
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It is a good starting point for understanding the fund landscape by size, stage, sector, and region.
Dineva tracked 55 new venture fund announcements and summarizes the results: the biggest funds (Bessemer at $5.75B, Bain Capital Ventures at $1.6B), median disclosed fund size ($70.4M), stage focus (47 funds invest at Seed), geography (Europe led by count, the U.S. by dollars), and sectors (28 funds mention AI). The post links to the underlying database.
For brands planning a campaign
Dineva turns a database into a narrative with clear categories. A B2B data platform could partner on co-branded monthly VC fund reports or use this format to surface its own data to founders.
Dineva offers actionable, founder-focused coverage of a specific funding opportunity, explaining what the NSF Seed Fund offers and how to get started with concrete numbers and a deadline.
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Dineva explains the U.S.
National Science Foundation's Seed Fund program for deep tech founders.
The post lists funding amounts (up to $305K for Phase I, up to $1.25M for Phase II, up to $1.555M through Fast Track, and roughly $2M with supplements), eligible technology areas, and the fact that NSF does not take equity.
It gives the next full proposal deadline (November 4, 2026) and points to an EverythingStartups VC fund database.
For brands planning a campaign
Dineva covers non-dilutive government funding alongside venture capital. A platform could partner on content that helps founders find grants and other non-VC capital, reaching an audience that may not fit traditional VC funding.
Monthly breakdowns of newly announced venture funds
A second recurring format aggregates new venture fund announcements for a month and breaks them down by total capital, largest funds, median disclosed fund size, sector focus, investment stage and geography.
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In the example post, Dineva reports 55 new funds and $16.8B of capital for September 2026, notes that five funds accounted for 61% of tracked capital, and breaks counts down by sector (AI, fintech, deeptech, climate, healthcare, defense, cybersecurity) and stage (seed, pre-seed, Series A, B, growth). It ends with a link to a VC funds database listing stages, sectors, check sizes and partner contact details.
Aggregate counts and dollar totals, largest funds ranked, then distributions by sector, stage and region, with median sizes alongside headline sums.
Dineva states the fund count and identifies the data source, but the figures cover funds tracked in one database, not the whole market. Buyers should ask how the database defines and tracks funds.
For brands planning a campaign
This is a natural fit for a funding-intelligence partnership: the format depends on a maintained dataset, so a co-marketing arrangement could center on data sharing, a co-branded monthly edition or access to the fund-level detail Dineva already publishes. Its stage and sector taxonomies offer a ready-made segmentation scheme for a joint report.
Alerts on non-dilutive government funding for founders
A third format is a practical funding-opportunity notice for deep tech founders.
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The example post walks through the U.S.
National Science Foundation’s Seed Fund program: Phase I support up to $305K, Phase II up to $1.25M, a Fast Track pathway up to $1.555M and supplemental funding that can bring total support to roughly $2M, with no equity taken.
It lists the technology areas the program covers, describes the Project Pitch step and required fields, and names the next full proposal deadline of November 4, 2026.
Program mechanics first: dollar amounts by phase, what the money covers, then the application sequence and a dated deadline.
For brands planning a campaign
A partner serving deep tech founders could provide eligibility details, application support or deadline reminders where readers are deciding whether to apply. The format covers program-level practical details as well as market statistics, broadening the range of suitable partners.
AI runs through both the roundup and fund-tracking formats.
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In the roundup, most of the ten largest rounds are AI companies or AI infrastructure, and Dineva flags more than $22.7B in combined valuation across four companies.
In the fund breakdown, 28 tracked funds name AI in their investment focus.
Dineva observes that AI increasingly appears alongside infrastructure, robotics, defense, healthcare and manufacturing rather than as a standalone category.
Repeated sector tagging across datasets, followed by an explicit observation about how AI is combining with other categories.
For brands planning a campaign
A partner whose product touches AI capital flows can build on a theme Dineva already treats as the main story. Partners outside AI should bring their own framing; the existing coverage does not provide a non-AI angle.
All three posts point readers to the same stack of free tools: the EverythingStartups newsletter, a VC funds database described as updated daily with funds, stages, sectors, check sizes and partner contacts, and a separate list of more than 45 startup accelerators.
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The posts provide useful detail, then link to the fuller dataset.
Consistent end-of-post call to action with a direct link, plus recurring mentions of the newsletter as the home for the data.
For brands planning a campaign
Dineva already runs a resource funnel, so a partnership can center on a destination rather than a single post: a jointly maintained database, a co-branded newsletter edition or a resource listed in the existing stack. Partners should plan where the link and resource will appear, not just how the post will read.
Ivelina Dineva's posts consistently direct readers to EverythingStartups funding resources, including a VC funds database and newsletter.
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The relationship is recorded as a disclosed partnership, but the posts do not specify their role; buyers should ask for clarification. Other companies and VC firms appear as subjects of funding news, not as sponsors or clients.
EverythingStartups
Disclosed partnership
Posts direct founders and readers to the EverythingStartups new VC funds database and newsletter, including a daily-updated fund database.
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The posts do not say whether Dineva owns, works for, or is otherwise formally affiliated with EverythingStartups; buyers should ask about their relationship.
Dineva’s weekly list of the ten biggest U.S. startup rounds shows who is raising and who is backing them; the monthly fund breakdown gives the market-wide view, with total capital, the largest funds, and counts by sector, stage and region.
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Read the pair together: the rounds list shows where money landed last week, while the fund list shows who is raising money to invest next.
Dineva also posts practical funding notices, such as the U.S.
National Science Foundation’s Seed Fund, worth checking when weighing non-dilutive money against venture capital.
The posts are short and number-dense, and almost all close with a question or a pointer to the EverythingStartups newsletter, a venture fund database and a list of accelerators, putting the next step one click away.
Degnan writes from inside the AI-security corner that Dineva covers as an investment theme: they joined an AI endpoint-security company as Chief Information Security Officer and hosted an AI Security Demo Day with Andreessen Horowitz, the kind of sector and investor event reflected in Dineva’s funding data.
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Degnan's posts are first-person career and event announcements from an operator, not market data or aggregation. Someone following Dineva for funding patterns will not find numbers, series or deal counts here.
Peiris publishes the individual events Dineva aggregates: announcing a $47M Series A led by Andreessen Horowitz and explaining why CRM data is not structured for AI agents.
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These are the kinds of AI-company round and argument found in Dineva’s roundups.
Peiris writes as a founder about one company and its product, using longer explanatory posts rather than tracking the wider market. For patterns across many funds and rounds, look to Dineva’s aggregation.
Ivelina Dineva’s three available posts—a weekly U.S. funding-round roundup, a September venture-fund tally, and a National Science Foundation seed-fund notice—target founders, investors, and operators who follow early-stage funding data.
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A funding-intelligence platform would reach a similar audience.
The only audience metric available is reach: 75,057 LinkedIn followers as of October 7, 2026.
Before committing spend, ask for LinkedIn follower analytics or a recent audience survey broken out by seniority, industry, company size, and country, along with impressions, comments, and link clicks on comparable funding-data posts.
LinkedIn followers
75,057
LinkedIn profile record for Ivelina Dineva · 2026-10-07
The three available posts contain no partnership program, sponsor policy, rate card, or media kit.
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Nothing in the three available posts describes a paid placement format, so no format is listed here.
What the posts do show is Ivelina Dineva promoting their own resources — an EverythingStartups newsletter, a new VC funds database, and an accelerator list — from the body of the posts themselves, which is self-promotion rather than sponsorship inventory.
Individual partner names or hashtags would not by themselves establish a paid arrangement, and none appear in the posts.
The practical position for a buyer is therefore that a paid partnership would have to be negotiated from scratch rather than selected from a published menu: approach through the LinkedIn profile, open with the specific co-marketing idea, and treat scope, deliverables, and disclosure as things to agree rather than things already documented.
Because this account rests on three posts, it describes an absence of documented formats in those posts, not a claim that Dineva never accepts sponsorship.
If a sponsor page, rate card, or disclosed sponsored placement exists elsewhere, this section may not reflect it.
A B2B startup data platform expanding its funding-intelligence resources is the strongest match for Dineva’s posts, for structural rather than thematic reasons.
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Their three recurring formats rely on maintained datasets: a weekly list of the ten largest U.S. funding rounds with standardized fields for each entry, and a monthly tally of newly announced venture funds broken out by size, sector, stage and geography.
In the September 2026 fund post, they report 55 funds and $16.8B of capital, note that five funds accounted for 61% of tracked capital, and give a median disclosed fund size of $70.4M.
A data platform can supply inputs and co-brand this dataset-driven narrative rather than graft its product onto an unrelated topic.
The stage and sector taxonomy they publish (seed, pre-seed, Series A/B/growth; AI, fintech, deeptech, climate, healthcare, defense, cybersecurity) is a ready-made segmentation scheme for a joint report.
The audience points in the same direction: founders and investors who follow funding data, close to the readers a funding-intelligence product wants.
Their posts do not say whether there is a sponsor policy, rate card or media kit; ask for the terms, deliverables and disclosure requirements.
The audience section records reach only: 75,057 LinkedIn followers as of October 7, 2026, with no follower demographics or per-post engagement.
Ask for post-level analytics on comparable funding posts before committing budget.
The fit is strong on subject and format, but audience quality remains unproven until those analytics are reviewed.
When to choose a specialist
An enterprise cybersecurity vendor selling to security practitioners is a poor fit based on Dineva’s posts.
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They cover cybersecurity only as a category within fund data: the September fund post counts seven funds naming cybersecurity, alongside seven in defense and eleven in deeptech, and the roundup lists one AI cybersecurity round.
These are capital-flow facts for founders and investors tracking where money is going; none of the posts explains a security problem, evaluates a tool or addresses a practitioner deciding what to buy.
Reaching security buyers through this material would require the vendor to supply its own problem framing and audience—a different creator’s job.
The only audience figure available is a follower count, with no engagement or seniority breakdown, so ask for those metrics before underwriting a campaign aimed at a specific job function.
This also limits other campaign needs: partners outside AI should expect to bring their own angle, since AI is the lens applied to both the roundup and the fund breakdown.
The posts describe no sponsorship inventory, so ask about placement, scope and disclosure before proceeding.
A worked campaign example
Use the monthly fund breakdown as the starting point: it is the format most visibly dependent on a maintained dataset.
The deliverable would be a co-branded monthly edition of the new-venture-fund report, published in the first week of the following month.
The platform supplies the fund dataset, and Dineva writes the narrative readers already expect: total capital raised, the largest funds and their share of tracked capital, median disclosed fund size, and spreads by sector, stage and geography.
The platform’s resource link sits in the closing call to action beside the existing VC funds database link.
The closing question becomes a partner-relevant prompt—for example, which newly raised fund readers would most want on their cap table—turning the post into a comment thread the platform can reuse.
Add one data point only the platform can publish, such as the share of new funds whose stated check sizes match the stages Dineva already reports, so the co-branded edition offers something the standalone post cannot.
The posts do not say whether a rate card or media kit is available, so agree on scope, fee, deliverable and disclosure wording in writing before the first edition.
Open the approach with the specific co-marketing idea and the data the platform brings, not a general sponsorship ask.
Run one edition as a test and compare it with Dineva’s two prior fund posts on impressions, link clicks, comments and saves, then decide whether to proceed with a three-edition run.
The same structure transfers to the weekly roundup, where the platform could supply round-level detail behind the leaderboard, but the monthly format is the cleaner first test because the dataset is the whole point of the post.
Choose Drecksler over Dineva when the platform is expanding deal-level news for commerce and fintech operators, not fund formation for founders and investors.
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Drecksler's April 6, 2026 roundup is the closest format match for a data platform to co-brand: a dated list of raises, acquisitions and IPO filings with round sizes and valuations — OpenAI's $122B round at a $730B pre-money, Allbirds' $39M asset sale, SpaceX's confidential IPO filing — plus valuation and backer detail for each item, in a subscription newsletter.
A deals database can feed that list, and the closing subscription prompt gives the platform a standing co-branded surface.
The August 31, 2026 post on Amazon's delivery-fee structure shows the deal list is paired with independent analysis rather than pure aggregation — the model a co-branded edition would draw on.
The beat is e-commerce and fintech news, not venture fund announcements broken out by stage, sector and geography.
A platform partnering here supplies deal data — rounds, acquisitions, IPO filings — rather than fund data, and the audience skews toward sellers and commerce operators rather than founders and LPs tracking where capital is going.
The distributable asset is an email list, not a database product, so co-branding means a newsletter credit or sponsored edition rather than a tool link.
There’s no published rate card, per-post engagement data or audience composition; ask for these and agree on terms for a test edition to compare reach and response.
Degnan is the better choice when the platform targets security practitioners and buyers and wants a named executive voice and event access rather than recurring data-driven publishing.
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The May 13, 2026 post offers a concrete template: co-hosting an Andreessen Horowitz AI Security Demo Day fireside chat on investing in security in the AI era, with demos from five portfolio companies and a closing reception with founders and the investing team.
An investor-data platform could add a live data readout or co-authored briefing to an event like this.
The August 19, 2026 post announcing a CISO appointment at Glow offers a second anchor at the buyer level rather than the publisher level.
There is no standing funding-data format to join.
Degnan's two posts are a personal transition announcement and an event invitation; neither tabulates rounds, funds, sectors or stages, and neither shows a weekly or monthly publishing cadence a partner could plan around.
A partnership would mean event sponsorship or executive commentary that the platform would have to build from scratch, on a security beat the funding-intelligence product does not obviously serve.
Cadence, audience size, engagement and commercial availability are not established; ask for those details before testing a single event, and proceed only if the platform can supply the funding-data angle itself.
Peiris is the better choice when the platform wants a first-person operator account of why structured data matters, rather than a curator who ranks a list.
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The March 26, 2026 post lays out a complete technical argument: the problem is not MCP or the model, but CRM, transcript and event data that isn’t structured for reasoning.
The fix is schema normalization, duplicate and merge-conflict resolution, entity enrichment, and replay into a single chronological timeline per customer, run in a persistent Python sandbox rather than a context window.
For a data platform, that makes Peiris a natural technical collaboration or customer-story partner; the post itself ends by inviting readers to test the setup in under an hour.
The September 9, 2026 Series A announcement ($47M led by Andreessen Horowitz, over 5,000 companies signed up since November) offers a second surface at the same account, bringing funding news and data-infrastructure detail together.
There is no standing roundup to slot a dataset into.
Peiris's posts are a company funding announcement and a product explainer — corporate voice and company news, not independent editorial curation — so any co-branded item would read as a vendor collaboration and should be labelled and framed as one.
Publishing is irregular and driven by company milestones, not a schedule.
Reach, audience composition and willingness to publish third-party material are unknown; ask about these before proceeding.
The platform would also have to supply the editorial format and distribution.
